CN102158365A - User clustering method and system in weblog mining - Google Patents
User clustering method and system in weblog mining Download PDFInfo
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- CN102158365A CN102158365A CN2011101315113A CN201110131511A CN102158365A CN 102158365 A CN102158365 A CN 102158365A CN 2011101315113 A CN2011101315113 A CN 2011101315113A CN 201110131511 A CN201110131511 A CN 201110131511A CN 102158365 A CN102158365 A CN 102158365A
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Abstract
The invention discloses a user clustering method and system in weblog mining for improving the service quality of a network station. The method comprises the following steps of: preprocessing weblogs to obtain credible weblogs; according to the access interest of a user and the credible weblogs, establishing a user access mode matrix expressing whether the user accesses a characteristic webpage; performing optimization clustering on the user access mode matrix by using a clustering algorithm on the basis of bacterial foraging optimization, and establishing a user common file according to a predetermined category of the user marked by a category number label; according to the user common file, extracting and storing the webpage with a prefetching probability exceeding the threshold of a predetermined prefetching probability into a buffer. Compared with the prior art, the precision is greatly improved.
Description
Technical field
The present invention relates to the user clustering technology, relate in particular to the user clustering method and system in a kind of network log excavation.
Background technology
Along with developing rapidly with extensively universal of Internet, the contradiction of the quick growth of information and people's attentiveness finiteness is in continuous increase, and how network user's growing interest can find most suitable information in the shortest time.The operator of each website also more and more wishes to understand the active situation of visitor in the website, excavates client activities information from the data ocean of huge customer group, allows the user can obtain personalized service.
Improve the influence power of website,, just should improve website structure according to user's browse mode with raising Web service quality, and finally realize the personalized recommendation of website for the user provides better service.
Summary of the invention
Technical problem to be solved by this invention is the user clustering technology that is to provide in a kind of network log excavation, reaches the purpose that improves the website service quality.
In order to solve the problems of the technologies described above, the present invention at first provides the user clustering method in a kind of network log excavation, comprises the steps:
Network log is carried out preliminary treatment, obtain the trustable network daily record;
According to user's visit interest and this trustable network daily record, set up and express the user access pattern the matrix whether user has visited the feature webpage;
Use is optimized cluster based on the clustering algorithm of flora optimization to this user access pattern matrix, and according to the classification under the default class label mark user, sets up user's public records;
According to this user's public records, the page that the probability of looking ahead is surpassed the default probability threshold value of looking ahead extracts and is saved in the buffer memory.
Wherein, this network log is carried out pretreated step, comprising:
This network log is carried out data cleansing, User Recognition and session identification.
Wherein, the step to this network log carries out this data cleansing comprises:
Picture in the filtering web page filters the webpage that dynamic web page and clicking rate are lower than default click threshold.
Wherein, use this clustering algorithm that this user access pattern matrix is carried out this optimization cluster,, set up the step of this user's public records, comprising according to the classification under this class label mark user based on flora optimization:
Use this clustering algorithm that this user access pattern matrix is optimized cluster, obtain the position of cluster centre based on flora optimization;
According to user and each distances of clustering centers, adopt the affiliated classification of this class label mark user, set up this user's public records according to the classification under the user.
The present invention also provides the user clustering system in a kind of network log excavation, comprising:
Pretreatment module is used for network log is carried out preliminary treatment, obtains the trustable network daily record;
First sets up module, is used for visit interest and this trustable network daily record according to the user, sets up and expresses the user access pattern the matrix whether user has visited the feature webpage;
Second sets up module, is used to use the clustering algorithm based on flora optimization that this user access pattern matrix is optimized cluster, and according to the classification under the default class label mark user, sets up user's public records;
The preextraction module is used for according to this user's public records, and the page that the probability of looking ahead is surpassed the default probability threshold value of looking ahead extracts and is saved in the buffer memory.
Wherein, this pretreatment module is used for this network log is carried out data cleansing, User Recognition and session identification, obtains this trustable network daily record.
Wherein, this pretreatment module is used for the picture of filtering web page, filters the webpage that dynamic web page and clicking rate are lower than default click threshold.
Wherein, this second is set up module and comprises:
Cluster cell is used to use this clustering algorithm based on flora optimization that this user access pattern matrix is optimized cluster, obtains the position of cluster centre;
Set up the unit, be used for, adopt the affiliated classification of this class label mark user, set up this user's public records according to the classification under the user according to user and each distances of clustering centers.
Compared with prior art, the present invention has the following advantages:
At network log magnanimity, higher-dimension, the various characteristics of data scale, the BF-C technology of optimizing based on swarm intelligence that the present invention proposes has good in convergence effect, is applicable to that the class that comprises has the data set of a plurality of sizes and density, is applicable to the advantage of high dimensional data.These advantages can solve the difficult problem of network user's cluster, can make cluster result more accurately with stable.Simultaneously, group's webpage that the present invention proposes scheme of looking ahead is compared with existing prefetching technique, and accuracy rate has increased significantly.
Technical scheme of the present invention can be used for numerous general or special purpose computingasystem environment or configuration.For example: personal computer, server computer, multicomputer system, network PC, mainframe computer, comprise distributed computing environment (DCE) of above any system or equipment or the like.
Description of drawings
Fig. 1 is the schematic flow sheet of the user clustering method during embodiment of the invention network log excavates;
Fig. 2 is the composition schematic diagram of the user clustering system during embodiment of the invention network log excavates.
Embodiment
Describe embodiments of the present invention in detail below with reference to drawings and Examples.
The present invention has designed a kind of based on network user's clustering method and the webpage pre-fetching system optimized, reduces the response time to reach, and improves the purpose of website service quality.
The Web daily record data has the characteristics of himself, and, renewal speed big as quantity is fast, complex structure etc.It is a lot of to the research that Web user carries out cluster to use traditional clustering method, but does not have special high-efficiency method, and the result of gained also is difficult to the basis as user-customized recommended.It is a kind of optimisation technique based on colony that flora is optimized (BF) algorithm, has that algorithm is simple, fast convergence rate, and the characteristics that required priori is few in optimizing process, need not the gradient information of object, have stronger versatility.The clustering algorithm of optimizing based on flora (BF-C) has good experiment effect on extensive high dimensional data, its cluster result is stable, insensitive to the center initial value, be used in the data set that classification varies in size, and can find the characteristics of globally optimal solution to meet the particular demands of Web user clustering.
The user access pattern matrix is as the input matrix of BF-C algorithm.Under the prerequisite of given clusters number k, the execution in step of BF-C algorithm is as follows:
1. initialization.Before the BF-C algorithm begins iteration, need the parameter of its algorithm be set in advance, and compose and give their certain initial values.Make t=1, and in the search volume, generate the position of S bacterium at random for each cluster centre.
2. iteration begins, and makes t=t+1, and each bacterium individuality is according to the iterative equation displacement of overturning.After each iteration, the cost of calculating target function, the relatively cost value in the current cost of bacterium and the iterative step before, if currency than before target function value little and do not reach the greatest iteration step number, then advance, upgrade the position of current bacterium, and upgrade the cost value of target function.Choose Euclidean distance in the calculating and measure the distance of each bacterium in data space.
3. each bacterium is carried out breeding, migration course in proper order, and upgrades the position.
4. carry out step number when algorithm and reach default greatest iteration step number Istep, algorithm stops, and forwards for the 5th step to, otherwise returns for the 2nd step.
5. labeled clusters center.After iteration stops, the some spots of algorithmic statement in the space, promptly all bacteriums all can move to the several fixed positions in the data space, and these several points are exactly the cluster centre that clustering algorithm finally obtains.
6. dividing data obtains cluster result.According to the cluster centre that obtains,, each data markers of data centralization in corresponding class, is obtained final cluster result according to the principle of minimum distance.
User clustering method during embodiment one, a kind of network log excavate
As shown in Figure 1, present embodiment mainly comprises the steps:
Step S110 carries out preliminary treatment to network log, obtains the trustable network daily record; This preliminary treatment mainly comprises data cleansing, User Recognition and session identification; Data cleansing wherein comprises the picture in the filtering web page, filters the webpage that dynamic web page and clicking rate are lower than default click threshold.
In the present embodiment, the clicking rate threshold value default for webpage is 2, and clicking rate generally is reflected as user's transient state action less than the webpage of this clicking rate threshold value, and attention rate that can not representative of consumer and browse interest.
Step S120 according to user's visit interest and this trustable network daily record, sets up and expresses the user access pattern the matrix whether user has visited the feature webpage.
Step S130 uses the clustering algorithm based on flora optimization that this user access pattern matrix is optimized cluster, and according to the classification under the default class label mark user, sets up user's public records;
Comprising: use the BF-C algorithm that the user access pattern matrix is optimized cluster, obtain the position of cluster centre; According to user and each distances of clustering centers, adopt the affiliated classification of default class label mark user, set up user's public records according to the classification under the user.
Which classification the user is included into through after the cluster, just can be endowed such other label.For example: 100 users just have 6 corresponding class labels through being divided into 6 classifications after the clustering algorithm cluster, and each user has its corresponding class label.
Step S140, according to this user's public records and the default probability threshold value of looking ahead, the page that the probability of will looking ahead in advance surpasses the probability threshold value of looking ahead extracts and is saved in the buffer memory of server, as the buffer memory page in the following user capture process.When the user is follow-up when conducting interviews, can reduce user's access time, improve the response speed of system, improve service quality.
For each classification of user, make P={p
1, p
2..., p
mBeing the collections of web pages that server end obtains, the webpage prefetch rules is defined as follows:
Wherein, P
1={ p
1, p
2..., p
xThe collections of web pages of having visited for the user, P
2={ q
1, q
2... q
jBe the collections of web pages of looking ahead, then
C is the probability threshold value of looking ahead, and is expressed as and has visited P
1Customer group in visited P
2User's ratio.
Data cleansing among the step S110 is disposed inconsistent, irrelevant data exactly from Web daily record data source, the Web daily record is converted into the reliable precise information that is fit to data mining, i.e. trustable network daily record.
At first from a plurality of servers, read the relevant Web daily record data of merging, analyze then and they are deposited in the corresponding data field.The attribute such as byte number, error code, user agent that comprises the URL page that IP address, user ID, user ask to visit, requesting method, access time, host-host protocol, transmission in the Web daily record data.User's once request may allow browser automatically download a plurality of adjuncts, and as some pictures etc., the All Files of download constitutes a page view, constitutes the situation of once asking corresponding a plurality of journal entries.
Data cleansing can reduce the Web log record according to analyzing, and mainly comprises the cleaning of following three aspects.
(1) URL extension name: in the general information website, just content page is relevant with user's request, (suffix is called gif to the page request of some picture categories, jpg etc.) and the script class file (suffix is called js, cgi, the file of css) can be considered to ask the file that has nothing to do, it should be deleted with the user.Because generally, the user can clearly not specify and go for whole pictures and the script file of asking on certain webpage, picture in the daily record and script file are to carry out the pictorial information that carries in the webpage of content for script of web page frame configuration mostly, when user's browsing pages word content, download automatically as ancillary documents, therefore, these pictures and script file can not actual response go out user's request behavior, will be removed in the data cleansing process.
(2) action: the GET action is the action of user requests webpage, and waiting as POST (POST is generally the action of user's submission form) action of other then can filter out, and keeps the action of user requests webpage.
(3) conditional code: the result of conditional code indication user request, with the expression request success of 2 beginnings, as 200 expression Transaction Success, 206 expression servers have been finished the GET request of certain customers; Expression requests with 3 beginnings are successfully turned to, and find the page of request as 302 expressions, 303 expression suggestion other URL of client-access or adopt other modes, and 305 expression requested resource must obtain from the address of server appointment; Expression link with 4 beginnings makes mistakes, as 400 expression false request (as syntax error), and 401 expression request authorization failures; Expressions with 5 beginnings produce server errors, produce internal errors as 500 expression servers, and 501 expression servers are not supported the function of asking.When carrying out data cleansing, should filter out information with 4 and 5 beginnings; In a word, filter request mistake and produce the information of server error, and obtain or keep the information of ask successfully and asking quilt successfully to be turned to.
User Recognition among the step S110.If carry out cluster analysis to the excavation of user access pattern or to the user, it is most important that the User Recognition problem then seems, because colony is made up of individuality, having only has more clearly understanding to individuality, can discern the feature of colony.Because local cache, the existence of acting server and fire compartment wall makes User Recognition become very complicated.The method of User Recognition mainly contains IP address and agency (agent) at present, embeds session identification (sessionID), registration, and Cookie, agent software is revised several methods such as browser.Through after the User Recognition, select n user.
Session identification among the step S110.Session is meant the page sequence that same user asks continuously in a navigation process, it has represented the once effectively visit of user to server.Session identification (Session Identification) is after User Recognition, the access sequence of each user in a period of time is decomposed, thereby obtain corresponding session.Obviously the page of different user request belongs to different sessions.Session recognition methods commonly used is an overtime method, promptly sets timeout threshold.The time threshold of system default is 30 minutes.
The application of clustering algorithm need be carried out formalization representation to the data in the Web daily record for convenience, makes it become the understandable input form of clustering algorithm.
Above-mentioned steps S120 specifically can be divided into the feature webpage and extract and set up two processes of user access pattern matrix.
Choose in this process at the feature webpage, the page that filters out the page of unique user request and only occur in a session from the trustable network daily record is formed an interest page set thereby obtain numerous user's interest pages.For excavating common user's interest, the user journal after the preliminary treatment needs further to filter.The page that has only a user to ask can't be represented the user's of colony interest, will be filtered; The page that occurs in the middle of a session simultaneously only can only reflect that user's transient state is paid close attention to, and lasting interest that can not representative of consumer also needs to be filtered.Through after the above processing, obtain an interest collections of web pages L={URL who forms by m user's interest web page address
1, URL
2..., URL
m, the webpage in the set of this interest page is just as the feature webpage of user clustering.
Set up this process of user access pattern matrix, on the basis of interest collections of web pages L, for each user who chooses sets up the browse mode vector.For j user (j=1,2 ..., n, n are total number of users), create a browse mode vector A
j={ R
1, R
2..., R
m, R wherein
i(i=1,2 ..., m, m are the number of feature webpage) and be a two-valued variable, represent whether this user visited feature webpage URL
iIf this user has asked URL
i, R
iValue be 1; Otherwise, R
iValue is 0.A as can be seen
jRepresent user j whether to visit webpage among the interest page set L, can reflect this user's the behavior of browsing, be referred to as the browse mode vector of unique user.Each user's browse mode vector is integrated the user access pattern matrix A that to obtain a size be n * m.Each row of this user access pattern matrix is represented a user, and each row is represented each feature webpage, and the value of each element of user access pattern matrix is 1 or 0, represents whether certain user has clicked this feature webpage.This user access pattern matrix will be as the input of user clustering algorithm.
User clustering system during embodiment two, network log excavate
In conjunction with embodiment illustrated in fig. 1, present embodiment as shown in Figure 2 comprises that mainly pretreatment module 210, first sets up module 220, second and set up module 230 and preextraction module 240, wherein:
First sets up module 220, links to each other with this pretreatment module 210, is used for visit interest and this trustable network daily record according to the user, sets up and expresses the user access pattern the matrix whether user has visited the feature webpage;
Second sets up module 230, first set up module 220 and link to each other with this, be used to use clustering algorithm that this user access pattern matrix is optimized cluster, and, set up user's public records according to the classification under the default classification number indicia user based on flora optimization;
Wherein, this pretreatment module 210 is used for this network log is carried out data cleansing, User Recognition and session identification, obtains this trustable network daily record.
Wherein, this pretreatment module 210 is used for the picture of filtering web page, filters the webpage that dynamic web page and clicking rate are lower than default click threshold.
Wherein, this second is set up module 230 and comprises:
Cluster cell is used to use this clustering algorithm based on flora optimization that this user access pattern matrix is optimized cluster, obtains the position of cluster centre;
Set up the unit, be used for, adopt the affiliated classification of this class label mark user, set up this user's public records according to the classification under the user according to user and each distances of clustering centers.
Though the disclosed execution mode of the present invention as above, the execution mode that described content just adopts for the ease of understanding the present invention is not in order to limit the present invention.Technical staff in any the technical field of the invention; under the prerequisite that does not break away from the disclosed spirit and scope of the present invention; can do any modification and variation what implement in form and on the details; but scope of patent protection of the present invention still must be as the criterion with the scope that appending claims was defined.
Claims (8)
1. the user clustering method during a network log excavates is characterized in that, comprises the steps:
Network log is carried out preliminary treatment, obtain the trustable network daily record;
According to user's visit interest and this trustable network daily record, set up and express the user access pattern the matrix whether user has visited the feature webpage;
Use is optimized cluster based on the clustering algorithm of flora optimization to this user access pattern matrix, and according to default classification number label, the classification under the mark user is set up user's public records;
According to this user's public records, the page that the probability of looking ahead is surpassed the default probability threshold value of looking ahead extracts and is saved in the buffer memory.
2. method according to claim 1 is characterized in that, this network log is carried out pretreated step, comprising:
This network log is carried out data cleansing, User Recognition and session identification.
3. method according to claim 2 is characterized in that, the step to this network log carries out this data cleansing comprises:
Picture in the filtering web page filters the webpage that dynamic web page and clicking rate are lower than default click threshold.
4. method according to claim 1, it is characterized in that, use this clustering algorithm that this user access pattern matrix is carried out this optimization cluster, according to the classification under this class label mark user based on flora optimization, set up the step of this user's public records, comprising:
Use this clustering algorithm that this user access pattern matrix is optimized cluster, obtain the position of cluster centre based on flora optimization;
According to user and each distances of clustering centers, adopt the affiliated classification of this class label mark user, set up this user's public records according to the classification under the user.
5. the user clustering system during a network log excavates is characterized in that, comprising:
Pretreatment module is used for network log is carried out preliminary treatment, obtains the trustable network daily record;
First sets up module, is used for visit interest and this trustable network daily record according to the user, sets up and expresses the user access pattern the matrix whether user has visited the feature webpage;
Second sets up module, is used to use the clustering algorithm based on flora optimization that this user access pattern matrix is optimized cluster, and according to the classification under the default classification number indicia user, sets up user's public records;
The preextraction module is used for according to this user's public records, and the page that the probability of looking ahead is surpassed the default probability threshold value of looking ahead extracts and is saved in the buffer memory.
6. system according to claim 5 is characterized in that:
This pretreatment module is used for this network log is carried out data cleansing, User Recognition and session identification, obtains this trustable network daily record.
7. system according to claim 6 is characterized in that:
This pretreatment module is used for the picture of filtering web page, filters the webpage that dynamic web page and clicking rate are lower than default click threshold.
8. system according to claim 1 is characterized in that, this second is set up module and comprise:
Cluster cell is used to use this clustering algorithm based on flora optimization that this user access pattern matrix is optimized cluster, obtains the position of cluster centre;
Set up the unit, be used for, adopt the affiliated classification of this class label mark user, set up this user's public records according to the classification under the user according to user and each distances of clustering centers.
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| CN102843743A (en) * | 2012-07-18 | 2012-12-26 | 南京邮电大学 | Hierarchical routing method of wireless sensor network based on flora foraging optimization algorithm |
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| CN103399855A (en) * | 2013-07-01 | 2013-11-20 | 百度在线网络技术（北京）有限公司 | Behavior intention determining method and device based on multiple data sources |
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| CN105227971B (en) * | 2015-07-09 | 2019-01-08 | 传成文化传媒（上海）有限公司 | A kind of information recommendation method and system of hotel's TV |
| CN106210044A (en) * | 2016-07-11 | 2016-12-07 | 焦点科技股份有限公司 | A kind of any active ues recognition methods based on the behavior of access |
| CN106210044B (en) * | 2016-07-11 | 2019-06-11 | 焦点科技股份有限公司 | A kind of any active ues recognition methods based on access behavior |
| CN107193744A (en) * | 2017-05-25 | 2017-09-22 | 中央民族大学 | A kind of Web application performance test flows based on daily record describe method |
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| CN112733060B (en) * | 2021-01-13 | 2023-12-01 | 中南大学 | Cache replacement method and device based on session cluster prediction and computer equipment |
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Application publication date: 20110817 |