CN105095482A - Data mining method and system for detecting abnormal data interval - Google Patents
Data mining method and system for detecting abnormal data interval Download PDFInfo
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- CN105095482A CN105095482A CN201510498223.XA CN201510498223A CN105095482A CN 105095482 A CN105095482 A CN 105095482A CN 201510498223 A CN201510498223 A CN 201510498223A CN 105095482 A CN105095482 A CN 105095482A
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Abstract
The invention provides a data mining method for detecting an abnormal data interval. The method comprises the steps that before data mining is performed on target data, the interval width and the stepping length need to be determined; when data mining is performed on the target data, the target data are traversed in an interval-stepping mode, the maximum deviation amount of the target data is obtained dynamically step by step, the deviation rate of the current data interval is determined, and the deviation rate of the current data interval is compared with a rated deviation rate; when the deviation rate of the current data interval is larger than the rated deviation rate, abnormal data points and abnormal data intervals in the current data interval are determined; after all the target data are traversed, all the abnormal data points, the abnormal data intervals and the overall maximum and minimum values of the target data are obtained, and the abnormal intervals in the target data can be accurately and rapidly detected.
Description
Technical field
The application relates to Data Mining, particularly a kind of data digging method and system detecting abnormal data interval.
Background technology
Along with the development of technology, the data mining of people to abnormal data interval is more and more paid close attention to.
Existing abnormal data data mining is all the detection carrying out outlier or isolated point, for the data with time or space continuity, abnormal independent digit strong point also can only be detected, abnormal data interval can not be detected.
Therefore, how effectively to detect that abnormal data interval is the current technical issues that need to address of those skilled in the art.
Summary of the invention
Technical problems to be solved in this application are to provide a kind of data digging method and the system that detect abnormal data interval, solving abnormal data data mining in prior art is all the detection carrying out outlier or isolated point, for the data with time or space continuity, also abnormal independent digit strong point can only be detected, the problem of abnormal data interval can not be detected.
Its concrete scheme is as follows:
Detect the data digging method in abnormal data interval, determine interval width and stepping length, travel through target data in the mode of interval stepping, the method comprises:
Obtain maximal value and the minimum value in current data interval;
Calculate the side-play amount in described current data interval, wherein, the side-play amount in described current data interval is the described maximal value in current data interval and the difference of minimum value;
Obtain current overall maximal value and the current overall minimum value of all data in the data and described current data interval traveled through;
Calculate the current overall side-play amount of described target data, wherein, the current overall side-play amount of described target data is the difference of described current overall maximal value and current overall minimum value;
Calculate the deviation ratio in described current data interval, wherein, the deviation ratio in described current data interval is the side-play amount in described current data interval and the ratio of described current overall side-play amount;
The deviation ratio in described current data interval and nominal offset rate are compared;
When the deviation ratio in described current data interval is greater than described nominal offset rate, determine that in described current data interval, maximum value data point and minimum value data point and the data point between described maximum value data point and minimum value data point are exceptional data point, the interval of described maximum value data point and described minimum value data point composition is that abnormal data is interval;
Interval stepping is carried out with described stepping length and interval width, next data interval is interval as current data, repeat maximal value and the minimum value in described acquisition current data interval, until traveled through described target data, obtain overall maximum and the global minimum of the interval and described target data of all exceptional data points, abnormal data.
Above-mentioned method, optionally, described to determine in current data interval that maximum value data point and minimum value data point and the data point between described maximum value data point and minimum value data point are exceptional data point after, also comprise:
Described exceptional data point is classified;
Wherein, described exceptional data point to be classified, comprising:
The size of first exceptional data point and second exceptional data point in more described current abnormal data interval;
When described first exceptional data point is less than described second exceptional data point, determine that described first exceptional data point is the abnormal initial data point increased, last exceptional data point in described current abnormal data interval is the abnormal end data point increased;
When described first exceptional data point is greater than described second exceptional data point, determine that described first exceptional data point is the abnormal initial data point reduced, last exceptional data point in described current abnormal data interval is the abnormal end data point reduced.
Above-mentioned method, optionally, also comprises:
Detect all exceptional data points in the mode of traversal, dispel the exceptional data point not meeting preset requirement, determine final exceptional data point;
Wherein, the described mode with traversal detects all exceptional data points, dispels the exceptional data point not meeting preset requirement, determines final exceptional data point, comprising:
Judge that the position of current exceptional data point is whether after the overall maximum of described target data and the position of global minimum;
When the position of described current exceptional data point is after the overall maximum of described target data and the position of global minimum, judge the relation between distance between the exceptional data point that described current exceptional data point is identical with a upper classification and described stepping length;
When distance between the exceptional data point that described current exceptional data point is identical with a upper classification is greater than described stepping length, determine that described current exceptional data point is abnormity point;
When distance between the exceptional data point that described current exceptional data point is identical with a upper classification equals described stepping length, the classification of described current exceptional data point is added in the identical exceptional data point of a described upper classification, and delete described current exceptional data point;
When distance between the exceptional data point that described current exceptional data point is identical with a upper classification is less than described stepping length, in described current exceptional data point generic, dispel an invalid exceptional data point;
Using next exceptional data point as current exceptional data point, repeat said process, until traveled through all exceptional data points.
Above-mentioned method, optionally, also comprises:
When the position of described current exceptional data point is not after the overall maximum of described target data and the position of global minimum, judge whether the difference of the maxima and minima in the abnormal data interval belonging to described current exceptional data point is greater than the specified deviate of entirety of described target data;
When the difference of the maxima and minima in the abnormal data interval belonging to described current exceptional data point is greater than the specified deviate of the entirety of described target data, judge the relation between distance between the exceptional data point that described current exceptional data point is identical with a upper classification and described stepping length;
When the difference of the maxima and minima in the abnormal data interval belonging to described current exceptional data point is not more than the specified deviate of the entirety of described target data, determine that described current exceptional data point is invalid exceptional data point, dispel described current exceptional data point;
Wherein, the specified deviate of the entirety of described target data is the product of the overall maximum of described target data and the difference of global minimum and described nominal offset rate.
Above-mentioned method, optionally, described in described current exceptional data point generic, dispel an invalid exceptional data point, comprising:
Judge the classification of described current exceptional data point;
When described current exceptional data point is the abnormal initial data point increased, the exceptional data point that reservation queue is little, deletes the exceptional data point that sequence is large;
When described current exceptional data point is the abnormal end data point increased, the exceptional data point that reservation queue is large, deletes the exceptional data point that sequence is little;
When described current exceptional data point is the abnormal initial data point reduced, the exceptional data point that reservation queue is little, deletes the exceptional data point that sequence is large;
When described current exceptional data point is the abnormal end data point reduced, the exceptional data point that reservation queue is large, deletes the exceptional data point that sequence is little.
Detect the data digging system in abnormal data interval, this system comprises:
First determining unit, for determining interval width and stepping length, travels through target data in the mode of interval stepping;
First acquiring unit, for obtaining maximal value and the minimum value in current data interval;
First computing unit, for calculating the side-play amount in described current data interval, wherein, the side-play amount in described current data interval is the described maximal value in current data interval and the difference of minimum value;
Second acquisition unit, for obtaining current overall maximal value and the current overall minimum value of all data in the data and described current data interval that have traveled through;
Second computing unit, for calculating the current overall side-play amount of described target data, wherein, the current overall side-play amount of described target data is the difference of described current overall maximal value and current overall minimum value;
3rd computing unit, for calculating the deviation ratio in described current data interval, wherein, the deviation ratio in described current data interval is the side-play amount in described current data interval and the ratio of described current overall side-play amount;
First comparing unit, for comparing the deviation ratio in described current data interval and nominal offset rate;
Second determining unit, for when the deviation ratio in described current data interval is greater than described nominal offset rate, determine that in described current data interval, maximum value data point and minimum value data point and the data point between described maximum value data point and minimum value data point are exceptional data point, the interval of described maximum value data point and described minimum value data point composition is that abnormal data is interval;
Stepping unit, for carrying out interval stepping with described stepping length and interval width, next data interval is interval as current data, repeat maximal value and the minimum value in described acquisition current data interval, until traveled through described target data, obtain overall maximum and the global minimum of the interval and described target data of all exceptional data points, abnormal data.
Above-mentioned system, optionally, also comprises:
Taxon, for classifying to described exceptional data point;
Wherein, described taxon, comprising:
Second comparing unit, for the size of first exceptional data point and second exceptional data point in more described current abnormal data interval;
3rd determining unit, for when described first exceptional data point is less than described second exceptional data point, determine that described first exceptional data point is the abnormal initial data point increased, last exceptional data point in described current abnormal data interval is the abnormal end data point increased;
4th determining unit, for when described first exceptional data point is greater than described second exceptional data point, determine that described first exceptional data point is the abnormal initial data point reduced, last exceptional data point in described current abnormal data interval is the abnormal end data point reduced.
Above-mentioned system, optionally, also comprises:
Detecting unit, for detecting all exceptional data points in the mode of traversal, dispelling the exceptional data point not meeting preset requirement, determining final exceptional data point;
Wherein, described detecting unit, comprising:
First judging unit, for judging that the position of current exceptional data point is whether after the overall maximum of described target data and the position of global minimum;
Second judging unit, for when the position of described current exceptional data point is after the overall maximum of described target data and the position of global minimum, judge the relation between distance between the exceptional data point that described current exceptional data point is identical with a upper classification and described stepping length;
5th determining unit, when being greater than described stepping length for the distance between the exceptional data point that described current exceptional data point is identical with a upper classification, determines that described current exceptional data point is abnormity point;
Adding device, when equaling described stepping length for the distance between the exceptional data point that described current exceptional data point is identical with a upper classification, the classification of described current exceptional data point is added in the identical exceptional data point of a described upper classification, and delete described current exceptional data point;
Dispel unit, when being less than described stepping length for the distance between the exceptional data point that described current exceptional data point is identical with a upper classification, in described current exceptional data point generic, dispel an invalid exceptional data point;
6th determining unit, for using next exceptional data point as current exceptional data point, repeat said process, until traveled through all exceptional data points.
Above-mentioned system, optionally, also comprises:
3rd judging unit, for when the position of described current exceptional data point is not after the overall maximum of described target data and the position of global minimum, judge whether the difference of the maxima and minima in the abnormal data interval belonging to described current exceptional data point is greater than the specified deviate of entirety of described target data;
4th judging unit, when difference for the maxima and minima when the abnormal data interval belonging to described current exceptional data point is greater than the entirety of described target data specified deviate, judge the relation between distance between the exceptional data point that described current exceptional data point is identical with a upper classification and described stepping length;
7th determining unit, when difference for the maxima and minima when the abnormal data interval belonging to described current exceptional data point is not more than the entirety of described target data specified deviate, determine that described current exceptional data point is invalid exceptional data point, dispel described current exceptional data point;
Wherein, the specified deviate of the entirety of described target data is the product of the overall maximum of described target data and the difference of global minimum and described nominal offset rate.
Above-mentioned system, optionally, described in dispel unit, comprising:
5th judging unit, for judging the classification of described current exceptional data point;
First processing unit, during for being the abnormal initial data point increased when described current exceptional data point, the exceptional data point that reservation queue is little, deletes the exceptional data point that sequence is large;
Second processing unit, during for being the abnormal end data point increased when described current exceptional data point, the exceptional data point that reservation queue is large, deletes the exceptional data point that sequence is little;
3rd processing unit, during for being the abnormal initial data point reduced when described current exceptional data point, the exceptional data point that reservation queue is little, deletes the exceptional data point that sequence is large;
Fourth processing unit, during for being the abnormal end data point reduced when described current exceptional data point, the exceptional data point that reservation queue is large, deletes the exceptional data point that sequence is little.
What the application provided a kind ofly detects in the data digging method in abnormal data interval, before data mining is carried out to target data, need to determine interval width and stepping length, target data is being carried out in the process of data mining, target data is traveled through in the mode of interval stepping, dynamically progressively obtain the maximum offset of target data, determine the deviation ratio in current data interval, the deviation ratio in current data interval and nominal offset rate are compared, when the deviation ratio in described current data interval is greater than described nominal offset rate, determine the exceptional data point in described current data interval and abnormal data interval, after having traveled through all target datas, obtain overall maximum and the global minimum of the interval and described target data of all exceptional data points, abnormal data, can detect accurately and rapidly between the exceptions area in target data.
Accompanying drawing explanation
In order to be illustrated more clearly in the technical scheme in the embodiment of the present application, below the accompanying drawing used required in describing embodiment is briefly described, apparently, accompanying drawing in the following describes is only some embodiments of the application, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
Fig. 1 is a kind of process flow diagram detecting the data digging method embodiment in abnormal data interval of the application;
Fig. 2 is the target data that the application carries out data mining;
Fig. 3 is a kind of process flow diagram detecting another embodiment of data digging method in abnormal data interval of the application;
Fig. 4 is a kind of process flow diagram detecting the another embodiment of data digging method in abnormal data interval of the application;
Fig. 5 is a kind of structural representation detecting the data digging system embodiment in abnormal data interval of the application;
Fig. 6 is a kind of structural representation detecting another embodiment of data digging method in abnormal data interval of the application;
Fig. 7 is a kind of structural representation detecting the another embodiment of data digging method in abnormal data interval of the application.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present application, be clearly and completely described the technical scheme in the embodiment of the present application, obviously, described embodiment is only some embodiments of the present application, instead of whole embodiments.Based on the embodiment in the application, those of ordinary skill in the art are not making the every other embodiment obtained under creative work prerequisite, all belong to the scope of the application's protection.
With reference to figure 1, show a kind of process flow diagram detecting the data digging method embodiment in abnormal data interval of the application, can comprise the following steps:
Step S101: the maximal value and the minimum value that obtain current data interval.
In the application, adopt the mode of interval stepping to travel through target data, so before the data mining carrying out abnormal data interval, need the interval width determining to adopt in data mining process and stepping length.
Can get interval width in the application is W, W >=2, and W is larger, and the detection between exceptions area is more accurate, but efficiency can be lower.
Stepping length D, 1≤D < W/2, D is larger, and the detection between exceptions area is more accurate, but efficiency can be lower.
Interval width W and stepping length D according to data volume and can need from Row sum-equal matrix.
With interval width for 5, stepping length is 2 when carrying out the data mining in abnormal data interval, as shown in Figure 2, current data interval is first data interval, 5 data in first data interval are respectively 1239,23423,2323,2434 and 23243, maximal value in these 5 data and minimum value are respectively 23243 and 1239, and record the position of maximal value and minimum value.
In the application, be the common subset in last interval and interval next time between back zone, the length between back zone is 3, and the data between back zone are 2323,2434 and 23243, and the maximal value between back zone and minimum value are respectively 23243 and 2323.
Step S102: the side-play amount calculating described current data interval, wherein, the side-play amount in described current data interval is the described maximal value in current data interval and the difference of minimum value.
Utilize the maximal value obtained to deduct minimum value 23243-1239, the side-play amount in current data interval can be obtained.
Step S103: the current overall maximal value and the current overall minimum value that obtain all data in the data and described current data interval traveled through.
When described current data interval is first data interval, the overall maximum determined from these 5 data is 23243, and global minimum is 1239, and records the position of overall maximum and global minimum.
Step S104: the current overall side-play amount calculating described target data, wherein, the current overall side-play amount of described target data is the difference of described current overall maximal value and current overall minimum value.
The mode of general acquisition overall offset amount is data statistics, and the mode obtaining overall offset amount in the application is for dynamically progressively to obtain maximum offset.
Utilize overall maximum to deduct global minimum 23243-1239, current overall maximum and current overall minimum value can be obtained.
Step S105: the deviation ratio calculating described current data interval, wherein, the deviation ratio in described current data interval is the side-play amount in described current data interval and the ratio of described current overall side-play amount.
In the application, definition deviation ratio is the deviation ratio in current data interval is the side-play amount in described current data interval and the ratio of described current overall side-play amount.
(23243-1239)/(23243-1239)=1, obtains the deviation ratio of first data interval.
Step S106: the deviation ratio in described current data interval and nominal offset rate are compared.When the deviation ratio in described current data interval is greater than described nominal offset rate, perform step S107: determine that in described current data interval, maximum value data point and minimum value data point and the data point between described maximum value data point and minimum value data point are exceptional data point, the interval of described maximum value data point and described minimum value data point composition is that abnormal data is interval.When the deviation ratio in described current data interval is less than described nominal offset rate, illustrate that the data in current data interval do not exist exception.
In the application, setting nominal offset rate is 0.9, (23243-1239)/(23243-1239)=1>0.9, illustrate that current data interval exists abnormal data, maximum value data point 23243 in current data interval, minimum value data point 1239 and the data point 23423,2323 and 2434 between described maximum value data point and minimum value data point are exceptional data point, and the interval that maximum value data point 23243 and minimum value data point 1239 form is then that abnormal data is interval.
Step S108: judge whether to have traveled through all data intervals, when not traveled through all data intervals, perform step S109: carry out interval stepping with described stepping length and interval width, next data interval is interval as current data, repeat above-mentioned data mining process, until traveled through described target data, obtain overall maximum and the global minimum of the interval and described target data of all exceptional data points, abnormal data.
After first data interval has detected, with 2 for stepping length carries out the detection of second data interval, 5 data in second data interval are respectively 2323,2434,23243,34354 and 23123, maximal value during in second data interval the 4th data and the 5th data are respectively and between back zone and minimum value are compared, determine maximal value and the minimum value of second data interval, be respectively 34354 and 2323.
From first data interval and second data interval in all data, determine current overall maximal value and current overall minimum value, be respectively 34354 and 1239.
Maximal value between current back zone and minimum value are respectively 34354 and 23123.
The deviation ratio of second data interval calculated is (34354-2323)/(34354-1239)=0.97>0.9, then determine the minimum value data point 2323 in second data interval, maximum value data point 34354 and 2434 and 23243 be exceptional data point between maximum value data point and minimum value data point, the interval abnormal data of minimum value data point 2323 and maximum value data point 34354 composition is interval.
The rest may be inferred, repeats the above-mentioned testing process to current data interval, until traveled through all target datas, obtains overall maximum and the global minimum of the interval and described target data of all exceptional data points, abnormal data.
What the application provided a kind ofly detects in the data digging method in abnormal data interval, before data mining is carried out to target data, need to determine interval width and stepping length, target data is being carried out in the process of data mining, target data is traveled through in the mode of interval stepping, dynamically progressively obtain the maximum offset of target data, determine the deviation ratio in current data interval, the deviation ratio in current data interval and nominal offset rate are compared, when the deviation ratio in described current data interval is greater than described nominal offset rate, determine the exceptional data point in described current data interval and abnormal data interval, after having traveled through all target datas, obtain overall maximum and the global minimum of the interval and described target data of all exceptional data points, abnormal data, can detect accurately and rapidly between the exceptions area in target data.
In the application, describedly determine that in described current data interval, maximum value data point and minimum value data point and the data point between described maximum value data point and minimum value data point are exceptional data point, after the interval that described maximum value data point and described minimum value data point form is abnormal data interval, also comprise: classify to described exceptional data point, detailed process is:
The size of first exceptional data point and second exceptional data point in more described current abnormal data interval.
When described first exceptional data point is less than described second exceptional data point, determine that described first exceptional data point is the abnormal initial data point increased, last exceptional data point in described current abnormal data interval is the abnormal end data point increased.
When described first exceptional data point is greater than described second exceptional data point, determine that described first exceptional data point is the abnormal initial data point reduced, last exceptional data point in described current abnormal data interval is the abnormal end data point reduced.
For the exceptional data point determined in first abnormal data interval, first exceptional data point is 1239, first exceptional data point and second abnormal data 23423 are compared, due to 1239<23423, then determine that 1239 for the abnormal initial data point increased, being arranged in last exceptional data point 23243 of first abnormal data interval so is accordingly the abnormal end data point increased.
By said method, all exceptional data points detected all are classified, determines the classification belonging to each exceptional data point.
With reference to figure 2, show a kind of process flow diagram detecting another embodiment of data digging method in abnormal data interval of the application, all exceptional data points and abnormal data interval detected in last embodiment after, in the present embodiment, all exceptional data points are detected in the mode of traversal, dispel the exceptional data point not meeting preset requirement, determine final exceptional data point.
Detailed process is:
Step S201: judge that the position of current exceptional data point is whether after the overall maximum of described target data and the position of global minimum.
When the position of described current exceptional data point is after the overall maximum of described target data and the position of global minimum, perform step S202, when the position of described current exceptional data point is not after the overall maximum of described target data and the position of global minimum, perform step S206.
Step S202: judge the relation between distance between the exceptional data point that described current exceptional data point is identical with a upper classification and described stepping length.
If current exceptional data point is the abnormal initial data point increased, then determine the distance between described current exceptional data point and a upper abnormal initial data point increased, the exceptional data point obtained in last embodiment all carries sequence number, by the sequence number of the sequence number of the current exceptional data point exceptional data point identical with a upper classification, the distance between them can be determined, the stepping length of the distance between the sequence number determined and interval stepping is compared.
The sequence number of initial data point increased as the exception in first abnormal data interval be 1 and second data interval in the sequence number of initial data point that increases of exception be 3, using sequence number be the exceptional data point of 3 as current exceptional data point, so the distance of these two exceptional data points is 2.
Step S203: when the distance between the exceptional data point that described current exceptional data point is identical with a upper classification is greater than described stepping length, determine that described current exceptional data point is abnormity point.
Step S204: when the distance between the exceptional data point that described current exceptional data point is identical with a upper classification equals described stepping length, the classification of described current exceptional data point is added in the identical exceptional data point of a described upper classification, and delete described current exceptional data point.
Distance due to these two exceptional data points equals stepping length, so deletes the current exceptional data point that sequence number is 3.
Step S205: when the distance between the exceptional data point that described current exceptional data point is identical with a upper classification is less than described stepping length, in described current exceptional data point generic, dispels an invalid exceptional data point.
Using next exceptional data point as current exceptional data point, repeat said process, until traveled through all exceptional data points.
Step S206: judge whether the difference of the maxima and minima in the abnormal data interval belonging to described current exceptional data point is greater than the specified deviate of entirety of described target data.
Wherein, the specified deviate of the entirety of described target data is the product of the overall maximum of described target data and the difference of global minimum and described nominal offset rate.
When the difference of the maxima and minima in the abnormal data interval belonging to described current exceptional data point is greater than the specified deviate of the entirety of described target data, perform step S202; When the difference of the maxima and minima in the abnormal data interval belonging to described current exceptional data point is not more than the specified deviate of the entirety of described target data, perform step S207.
Step S207: determine that described current exceptional data point is invalid exceptional data point, dispel described current exceptional data point.
In the application, the exceptional data point determine above-described embodiment and abnormal data interval are further detected, identify between the exceptions area that the first step detects, dispel the data interval not meeting nominal offset amount, retain initial data and the end data of continuum, dispel the repeating data of continuum.Improve the accuracy identifying abnormal data, and identify abnormal data interval accurately.
With reference to figure 3, show a kind of process flow diagram detecting another embodiment of data digging method in abnormal data interval of the application, in described current exceptional data point generic, dispel an invalid exceptional data point, comprising:
Step S301: the classification judging described current exceptional data point.
Step S302: when described current exceptional data point is the abnormal initial data point increased, the exceptional data point that reservation queue is little, deletes the exceptional data point that sequence is large.
Step S303: when described current exceptional data point is the abnormal end data point increased, the exceptional data point that reservation queue is large, deletes the exceptional data point that sequence is little.
Step S304: when described current exceptional data point is the abnormal initial data point reduced, the exceptional data point that reservation queue is little, deletes the exceptional data point that sequence is large.
Step S305: when described current exceptional data point is the abnormal end data point reduced, the exceptional data point that reservation queue is large, deletes the exceptional data point that sequence is little.
Corresponding with the method that a kind of data digging method embodiment 1 detecting abnormal data interval of above-mentioned the application provides, see Fig. 4, present invention also provides a kind of data digging system embodiment 1 detecting abnormal data interval, in the present embodiment, this system comprises:
First determining unit 401, for determining interval width and stepping length, travels through target data in the mode of interval stepping.
First acquiring unit 402, for obtaining maximal value and the minimum value in current data interval.
First computing unit 403, for calculating the side-play amount in described current data interval, wherein, the side-play amount in described current data interval is the described maximal value in current data interval and the difference of minimum value.
Second acquisition unit 404, for obtaining current overall maximal value and the current overall minimum value of all data in the data and described current data interval that have traveled through.
Second computing unit 405, for calculating the current overall side-play amount of described target data, wherein, the current overall side-play amount of described target data is the difference of described current overall maximal value and current overall minimum value.
3rd computing unit 406, for calculating the deviation ratio in described current data interval, wherein, the deviation ratio in described current data interval is the side-play amount in described current data interval and the ratio of described current overall side-play amount.
First comparing unit 407, for comparing the deviation ratio in described current data interval and nominal offset rate.
Second determining unit 408, for when the deviation ratio in described current data interval is greater than described nominal offset rate, determine that in described current data interval, maximum value data point and minimum value data point and the data point between described maximum value data point and minimum value data point are exceptional data point, the interval of described maximum value data point and described minimum value data point composition is that abnormal data is interval.
Stepping unit 409, for carrying out interval stepping with described stepping length and interval width, next data interval is interval as current data, repeat above-mentioned data mining process, until traveled through described target data, obtain overall maximum and the global minimum of the interval and described target data of all exceptional data points, abnormal data.
This system comprises: taxon, for classifying to described exceptional data point.
Wherein, described taxon, comprising:
Second comparing unit, for the size of first exceptional data point and second exceptional data point in more described current abnormal data interval.
3rd determining unit, for when described first exceptional data point is less than described second exceptional data point, determine that described first exceptional data point is the abnormal initial data point increased, last exceptional data point in described current abnormal data interval is the abnormal end data point increased.
4th determining unit, for when described first exceptional data point is greater than described second exceptional data point, determine that described first exceptional data point is the abnormal initial data point reduced, last exceptional data point in described current abnormal data interval is the abnormal end data point reduced.
See Fig. 5, present invention also provides a kind of another embodiment of data digging system detecting abnormal data interval, in the present embodiment, this system also comprises detecting unit, for detecting all exceptional data points in the mode of traversal, dispel the exceptional data point not meeting preset requirement, determine final exceptional data point.
Wherein, described detecting unit comprises:
First judging unit 501, for judging that the position of current exceptional data point is whether after the overall maximum of described target data and the position of global minimum.
Second judging unit 502, for when the position of described current exceptional data point is after the overall maximum of described target data and the position of global minimum, judge the relation between distance between the exceptional data point that described current exceptional data point is identical with a upper classification and described stepping length.
5th determining unit 503, when being greater than described stepping length for the distance between the exceptional data point that described current exceptional data point is identical with a upper classification, determines that described current exceptional data point is abnormity point.
Adding device 504, when equaling described stepping length for the distance between the exceptional data point that described current exceptional data point is identical with a upper classification, the classification of described current exceptional data point is added in the identical exceptional data point of a described upper classification, and delete described current exceptional data point.
Dispel unit 505, when being less than described stepping length for the distance between the exceptional data point that described current exceptional data point is identical with a upper classification, in described current exceptional data point generic, dispel an invalid exceptional data point.
3rd judging unit 506, for when the position of described current exceptional data point is not after the overall maximum of described target data and the position of global minimum, judge whether the difference of the maxima and minima in the abnormal data interval belonging to described current exceptional data point is greater than the specified deviate of entirety of described target data.
4th judging unit 507, when difference for the maxima and minima when the abnormal data interval belonging to described current exceptional data point is greater than the entirety of described target data specified deviate, judge the relation between distance between the exceptional data point that described current exceptional data point is identical with a upper classification and described stepping length.
7th determining unit 508, when difference for the maxima and minima when the abnormal data interval belonging to described current exceptional data point is not more than the entirety of described target data specified deviate, determine that described current exceptional data point is invalid exceptional data point, dispel described current exceptional data point;
Wherein, the specified deviate of the entirety of described target data is the product of the overall maximum of described target data and the difference of global minimum and described nominal offset rate.
6th determining unit 509, for using next exceptional data point as current exceptional data point, repeat said process, until traveled through all exceptional data points.
See Fig. 6, present invention also provides a kind of another embodiment of data digging system detecting abnormal data interval, in the present embodiment, described in dispel unit, comprising:
5th judging unit 601, for judging the classification of described current exceptional data point.
First processing unit 602, during for being the abnormal initial data point increased when described current exceptional data point, the exceptional data point that reservation queue is little, deletes the exceptional data point that sequence is large.
Second processing unit 603, during for being the abnormal end data point increased when described current exceptional data point, the exceptional data point that reservation queue is large, deletes the exceptional data point that sequence is little.
3rd processing unit 604, during for being the abnormal initial data point reduced when described current exceptional data point, the exceptional data point that reservation queue is little, deletes the exceptional data point that sequence is large.
Fourth processing unit 605, during for being the abnormal end data point reduced when described current exceptional data point, the exceptional data point that reservation queue is large, deletes the exceptional data point that sequence is little.
In sum, the mode of the interval stepping of the application's usage data carries out data traversal, obtains again the maximum offset of data entirety dynamically, by judging whether interval deviation ratio is greater than specified deviation ratio and tentatively determines between exceptions area.By traveling through between the exceptions area tentatively determined, determining the true scope between exceptions area, dispelling invalid result.
The method can detect between the exceptions area of large data accurately and rapidly, and supports multithreading, multi-process, between secondary detection exceptions area.
It should be noted that, each embodiment in this instructions all adopts the mode of going forward one by one to describe, and what each embodiment stressed is the difference with other embodiments, between each embodiment identical similar part mutually see.For device class embodiment, due to itself and embodiment of the method basic simlarity, so description is fairly simple, relevant part illustrates see the part of embodiment of the method.
Finally, also it should be noted that, in this article, the such as relational terms of first and second grades and so on is only used for an entity or operation to separate with another entity or operational zone, and not necessarily requires or imply the relation that there is any this reality between these entities or operation or sequentially.And, term " comprises ", " comprising " or its any other variant are intended to contain comprising of nonexcludability, thus make to comprise the process of a series of key element, method, article or equipment and not only comprise those key elements, but also comprise other key elements clearly do not listed, or also comprise by the intrinsic key element of this process, method, article or equipment.When not more restrictions, the key element limited by statement " comprising ... ", and be not precluded within process, method, article or the equipment comprising described key element and also there is other identical element.
For convenience of description, various unit is divided into describe respectively with function when describing above device.Certainly, the function of each unit can be realized in same or multiple software and/or hardware when implementing the application.
As seen through the above description of the embodiments, those skilled in the art can be well understood to the mode that the application can add required general hardware platform by software and realizes.Based on such understanding, the technical scheme of the application can embody with the form of software product the part that prior art contributes in essence in other words, this computer software product can be stored in storage medium, as ROM/RAM, magnetic disc, CD etc., comprising some instructions in order to make a computer equipment (can be personal computer, server, or the network equipment etc.) perform the method described in some part of each embodiment of the application or embodiment.
Above to the application provide a kind of detect abnormal data interval data digging method and system be described in detail, apply specific case herein to set forth the principle of the application and embodiment, the explanation of above embodiment is just for helping method and the core concept thereof of understanding the application; Meanwhile, for one of ordinary skill in the art, according to the thought of the application, all will change in specific embodiments and applications, in sum, this description should not be construed as the restriction to the application.
Claims (10)
1. detect the data digging method in abnormal data interval, it is characterized in that, determine interval width and stepping length, travel through target data in the mode of interval stepping, the method comprises:
Obtain maximal value and the minimum value in current data interval;
Calculate the side-play amount in described current data interval, wherein, the side-play amount in described current data interval is the described maximal value in current data interval and the difference of minimum value;
Obtain current overall maximal value and the current overall minimum value of all data in the data and described current data interval traveled through;
Calculate the current overall side-play amount of described target data, wherein, the current overall side-play amount of described target data is the difference of described current overall maximal value and current overall minimum value;
Calculate the deviation ratio in described current data interval, wherein, the deviation ratio in described current data interval is the side-play amount in described current data interval and the ratio of described current overall side-play amount;
The deviation ratio in described current data interval and nominal offset rate are compared;
When the deviation ratio in described current data interval is greater than described nominal offset rate, determine that in described current data interval, maximum value data point and minimum value data point and the data point between described maximum value data point and minimum value data point are exceptional data point, the interval of described maximum value data point and described minimum value data point composition is that abnormal data is interval;
Interval stepping is carried out with described stepping length and interval width, next data interval is interval as current data, repeat maximal value and the minimum value in described acquisition current data interval, until traveled through described target data, obtain overall maximum and the global minimum of the interval and described target data of all exceptional data points, abnormal data.
2. method according to claim 1, it is characterized in that, described to determine in current data interval that maximum value data point and minimum value data point and the data point between described maximum value data point and minimum value data point are exceptional data point after, also comprise:
Described exceptional data point is classified;
Wherein, described exceptional data point to be classified, comprising:
The size of first exceptional data point and second exceptional data point in more described current abnormal data interval;
When described first exceptional data point is less than described second exceptional data point, determine that described first exceptional data point is the abnormal initial data point increased, last exceptional data point in described current abnormal data interval is the abnormal end data point increased;
When described first exceptional data point is greater than described second exceptional data point, determine that described first exceptional data point is the abnormal initial data point reduced, last exceptional data point in described current abnormal data interval is the abnormal end data point reduced.
3. method according to claim 2, is characterized in that, also comprises:
Detect all exceptional data points in the mode of traversal, dispel the exceptional data point not meeting preset requirement, determine final exceptional data point;
Wherein, the described mode with traversal detects all exceptional data points, dispels the exceptional data point not meeting preset requirement, determines final exceptional data point, comprising:
Judge that the position of current exceptional data point is whether after the overall maximum of described target data and the position of global minimum;
When the position of described current exceptional data point is after the overall maximum of described target data and the position of global minimum, judge the relation between distance between the exceptional data point that described current exceptional data point is identical with a upper classification and described stepping length;
When distance between the exceptional data point that described current exceptional data point is identical with a upper classification is greater than described stepping length, determine that described current exceptional data point is abnormity point;
When distance between the exceptional data point that described current exceptional data point is identical with a upper classification equals described stepping length, the classification of described current exceptional data point is added in the identical exceptional data point of a described upper classification, and delete described current exceptional data point;
When distance between the exceptional data point that described current exceptional data point is identical with a upper classification is less than described stepping length, in described current exceptional data point generic, dispel an invalid exceptional data point;
Using next exceptional data point as current exceptional data point, repeat said process, until traveled through all exceptional data points.
4. method according to claim 3, is characterized in that, also comprises:
When the position of described current exceptional data point is not after the overall maximum of described target data and the position of global minimum, judge whether the difference of the maxima and minima in the abnormal data interval belonging to described current exceptional data point is greater than the specified deviate of entirety of described target data;
When the difference of the maxima and minima in the abnormal data interval belonging to described current exceptional data point is greater than the specified deviate of the entirety of described target data, judge the relation between distance between the exceptional data point that described current exceptional data point is identical with a upper classification and described stepping length;
When the difference of the maxima and minima in the abnormal data interval belonging to described current exceptional data point is not more than the specified deviate of the entirety of described target data, determine that described current exceptional data point is invalid exceptional data point, dispel described current exceptional data point;
Wherein, the specified deviate of the entirety of described target data is the product of the overall maximum of described target data and the difference of global minimum and described nominal offset rate.
5. method according to claim 3, is characterized in that, described in described current exceptional data point generic, dispels an invalid exceptional data point, comprising:
Judge the classification of described current exceptional data point;
When described current exceptional data point is the abnormal initial data point increased, the exceptional data point that reservation queue is little, deletes the exceptional data point that sequence is large;
When described current exceptional data point is the abnormal end data point increased, the exceptional data point that reservation queue is large, deletes the exceptional data point that sequence is little;
When described current exceptional data point is the abnormal initial data point reduced, the exceptional data point that reservation queue is little, deletes the exceptional data point that sequence is large;
When described current exceptional data point is the abnormal end data point reduced, the exceptional data point that reservation queue is large, deletes the exceptional data point that sequence is little.
6. detect the data digging system in abnormal data interval, it is characterized in that, this system comprises:
First determining unit, for determining interval width and stepping length, travels through target data in the mode of interval stepping;
First acquiring unit, for obtaining maximal value and the minimum value in current data interval;
First computing unit, for calculating the side-play amount in described current data interval, wherein, the side-play amount in described current data interval is the described maximal value in current data interval and the difference of minimum value;
Second acquisition unit, for obtaining current overall maximal value and the current overall minimum value of all data in the data and described current data interval that have traveled through;
Second computing unit, for calculating the current overall side-play amount of described target data, wherein, the current overall side-play amount of described target data is the difference of described current overall maximal value and current overall minimum value;
3rd computing unit, for calculating the deviation ratio in described current data interval, wherein, the deviation ratio in described current data interval is the side-play amount in described current data interval and the ratio of described current overall side-play amount;
First comparing unit, for comparing the deviation ratio in described current data interval and nominal offset rate;
Second determining unit, for when the deviation ratio in described current data interval is greater than described nominal offset rate, determine that in described current data interval, maximum value data point and minimum value data point and the data point between described maximum value data point and minimum value data point are exceptional data point, the interval of described maximum value data point and described minimum value data point composition is that abnormal data is interval;
Stepping unit, for carrying out interval stepping with described stepping length and interval width, next data interval is interval as current data, repeat maximal value and the minimum value in described acquisition current data interval, until traveled through described target data, obtain overall maximum and the global minimum of the interval and described target data of all exceptional data points, abnormal data.
7. system according to claim 6, is characterized in that, also comprises:
Taxon, for classifying to described exceptional data point;
Wherein, described taxon, comprising:
Second comparing unit, for the size of first exceptional data point and second exceptional data point in more described current abnormal data interval;
3rd determining unit, for when described first exceptional data point is less than described second exceptional data point, determine that described first exceptional data point is the abnormal initial data point increased, last exceptional data point in described current abnormal data interval is the abnormal end data point increased;
4th determining unit, for when described first exceptional data point is greater than described second exceptional data point, determine that described first exceptional data point is the abnormal initial data point reduced, last exceptional data point in described current abnormal data interval is the abnormal end data point reduced.
8. system according to claim 7, is characterized in that, also comprises:
Detecting unit, for detecting all exceptional data points in the mode of traversal, dispelling the exceptional data point not meeting preset requirement, determining final exceptional data point;
Wherein, described detecting unit, comprising:
First judging unit, for judging that the position of current exceptional data point is whether after the overall maximum of described target data and the position of global minimum;
Second judging unit, for when the position of described current exceptional data point is after the overall maximum of described target data and the position of global minimum, judge the relation between distance between the exceptional data point that described current exceptional data point is identical with a upper classification and described stepping length;
5th determining unit, when being greater than described stepping length for the distance between the exceptional data point that described current exceptional data point is identical with a upper classification, determines that described current exceptional data point is abnormity point;
Adding device, when equaling described stepping length for the distance between the exceptional data point that described current exceptional data point is identical with a upper classification, the classification of described current exceptional data point is added in the identical exceptional data point of a described upper classification, and delete described current exceptional data point;
Dispel unit, when being less than described stepping length for the distance between the exceptional data point that described current exceptional data point is identical with a upper classification, in described current exceptional data point generic, dispel an invalid exceptional data point;
6th determining unit, for using next exceptional data point as current exceptional data point, repeat said process, until traveled through all exceptional data points.
9. system according to claim 8, is characterized in that, also comprises:
3rd judging unit, for when the position of described current exceptional data point is not after the overall maximum of described target data and the position of global minimum, judge whether the difference of the maxima and minima in the abnormal data interval belonging to described current exceptional data point is greater than the specified deviate of entirety of described target data;
4th judging unit, when difference for the maxima and minima when the abnormal data interval belonging to described current exceptional data point is greater than the entirety of described target data specified deviate, judge the relation between distance between the exceptional data point that described current exceptional data point is identical with a upper classification and described stepping length;
7th determining unit, when difference for the maxima and minima when the abnormal data interval belonging to described current exceptional data point is not more than the entirety of described target data specified deviate, determine that described current exceptional data point is invalid exceptional data point, dispel described current exceptional data point;
Wherein, the specified deviate of the entirety of described target data is the product of the overall maximum of described target data and the difference of global minimum and described nominal offset rate.
10. system according to claim 8, is characterized in that, described in dispel unit, comprising:
5th judging unit, for judging the classification of described current exceptional data point;
First processing unit, during for being the abnormal initial data point increased when described current exceptional data point, the exceptional data point that reservation queue is little, deletes the exceptional data point that sequence is large;
Second processing unit, during for being the abnormal end data point increased when described current exceptional data point, the exceptional data point that reservation queue is large, deletes the exceptional data point that sequence is little;
3rd processing unit, during for being the abnormal initial data point reduced when described current exceptional data point, the exceptional data point that reservation queue is little, deletes the exceptional data point that sequence is large;
Fourth processing unit, during for being the abnormal end data point reduced when described current exceptional data point, the exceptional data point that reservation queue is large, deletes the exceptional data point that sequence is little.
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