CN113688926A - Website behavior classification method, system, storage medium and equipment - Google Patents
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Abstract
The invention belongs to the field of website behavior classification, and provides a website behavior classification method, a website behavior classification system, a storage medium and a device. The method comprises the steps of obtaining a website behavior data set; wherein, one attribute of each data in the set is a dimension; screening the neighbors of each data to determine a filtering window of the corresponding data; randomly selecting a preset number of pieces of data from the website behavior data set as class center data respectively, and calculating the membership degree of each piece of data in the website behavior data set belonging to each class center data; based on the filtering window of each data, each dimension of each data is used as a guide to filter the membership degree, and the weighted sum of the membership degrees after multi-dimensional filtering is used as the final membership degree after filtering; updating each class center data by using the finally filtered membership degree, and further updating the attribute weight of each dimension; and (4) judging the termination condition of the step of updating each class center data through iterative calculation, and finally outputting a website behavior classification result.
Description
Technical Field
The invention belongs to the field of website behavior classification, and particularly relates to a website behavior classification method, a website behavior classification system, a storage medium and a device.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The guide filtering is an image filtering method that can effectively remove noise and maintain edge information of a guide image, and is widely used in image segmentation, enhancement, defogging, and the like. This technique generally uses an image to be processed as a guide image, and performs filtering processing on an input image using information of the guide image to obtain a filtered image having gradient information of the guide image and effectively removing noise. In recent years, in order to solve the problem that the traditional clustering algorithm cannot well utilize the spatial information of images to cause the inaccurate clustering segmentation result, a plurality of scholars apply a guided filtering method to the clustering process, and a plurality of fuzzy clustering algorithms related to guided filtering are provided. The methods take the image to be segmented as a guide image, and carry out filtering on the membership degree obtained by the fuzzy C mean value, so that the membership degree can contain more gradient information, and the accuracy of image segmentation is further improved.
In recent years, research efforts to incorporate guided filtering into blur clustering for image segmentation have received increasing attention. However, the current fuzzy clustering algorithm based on the guided filtering is only limited to the problem of image segmentation, and the guided filtering is mainly used for processing images and is not suitable for website behavior analysis data. The website behavior analysis data also has spatial information, and mining of the potential information of the data has important significance for more accurate classification of the data. However, the current fuzzy clustering method with spatial information is difficult to calculate or easily loses information in the clustering process.
Disclosure of Invention
In order to solve the technical problems in the background art, the present invention provides a method, a system, a storage medium, and a device for classifying website behaviors, which can accurately classify the website behaviors.
In order to achieve the purpose, the invention adopts the following technical scheme:
the first aspect of the present invention provides a website behavior classification method, which includes:
acquiring a website behavior data set; wherein, one attribute of each data in the set is a dimension;
screening the neighbors of each data to determine a filtering window of the corresponding data;
randomly selecting a preset number of pieces of data from the website behavior data set as class center data respectively, and calculating the membership degree of each piece of data in the website behavior data set belonging to each class center data;
based on the filtering window of each data, each dimension of each data is used as a guide to filter the membership degree, and the weighted sum of the membership degrees after multi-dimensional filtering is used as the final membership degree after filtering;
updating each class center data by using the finally filtered membership degree, and further updating the attribute weight of each dimension;
and (4) judging the termination condition of the step of updating each class center data through iterative calculation, and finally outputting a website behavior classification result.
Further, each data in the set contains at least two attributes.
Further, a K nearest neighbor method is used for finding K nearest data for each data in the website behavior data set, and the K data are neighbors of the corresponding data; k is a positive integer greater than or equal to 1.
Further, the process of finding the nearest k pieces of data for each piece of data in the website behavior data set is as follows:
calculating a distance matrix of the data using euclidean distances;
the nearest k neighbors including itself are found for each data.
Further, the process of determining the filter window of the corresponding data is:
screening the neighbors of each data point by using subtraction or addition to ensure that each data point and the neighbors thereof are neighbors;
each data point has its remaining neighbors with symmetry as a filtering window.
Further, the formula for filtering the membership degree by using each dimension of each datum as a guide is
Wherein u'ijmMembership, u, of the ith class filtered by the mth dimension of the jth dataijRepresenting degree of membership, x, of the jth data belonging to the ith classjmValue, ω, representing the mth dimension of the jth pilot datakWindow representing the derivative data centered on the kth data, akmAnd bkmThe representation window omegakLinear coefficients in the m-th dimension.
Further, the termination condition of the step of updating the respective class center data is: and iteratively calculating the difference value between the two adjacent set objective function values to be smaller than a set value or the iteration times to exceed a set threshold value.
A second aspect of the present invention provides a website behavior classification system, which includes:
the website behavior data acquisition module is used for acquiring a website behavior data set; wherein, one attribute of each data in the set is a dimension;
a filter window determination module for screening the neighbors of each data to determine a filter window for the corresponding data;
the class center data initialization module is used for randomly selecting a preset number of pieces of data from the website behavior data set as class center data respectively and calculating the membership degree of each piece of data in the website behavior data set belonging to each class center data;
the membership calculation module is used for filtering membership degrees based on a filtering window of each datum and using each dimension of each datum as a guide respectively, and weighting and summing the membership degrees after multidimensional filtering to be used as the final membership degree after filtering;
the attribute weight updating module is used for updating each class center data by utilizing the finally filtered membership degree so as to update the attribute weight of each dimensionality;
and the classification result output module is used for iteratively calculating and judging the termination condition of the step of updating each class center data and finally outputting the website behavior classification result.
A third aspect of the invention provides a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the method for website behavior classification as described above.
A fourth aspect of the invention provides a computer apparatus.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the website behavior classification method as described above when executing the program.
Compared with the prior art, the invention has the beneficial effects that:
the method comprises the steps of screening neighbors of each data in a website behavior data set to determine a filtering window of the corresponding data, randomly selecting a preset number of pieces of data from the website behavior data set to serve as class center data respectively, calculating the membership degree of each piece of data in the website behavior data set to belong to each class center data, filtering the membership degree respectively as a guide by using each dimension of each piece of data based on the filtering window of each piece of data, weighting and summing the membership degrees after multidimensional filtering to serve as the final membership degree after filtering, and mining interests and preferences of users more accurately during website behavior analysis by using guide filtering, so that the accuracy of website behavior classification is improved.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a diagram of data selection in a filter window according to an embodiment of the present invention;
FIG. 2 is a diagram of a guided filtering versus membership filtering process according to an embodiment of the present invention;
FIG. 3 is a detailed process of guided filtering for first class membership filtering according to an embodiment of the present invention;
fig. 4 is a flowchart of a website behavior classification method according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
As shown in fig. 4, the present embodiment provides a website behavior classification method, which specifically includes the following steps:
s101: acquiring a website behavior data set; wherein one attribute of each data in the set is a dimension.
Wherein each data in the set comprises at least two attributes.
Reading in website behavior data X ═ { X ] needing clustering1,x2,...,xNIn which xj＝{xj1,xj2,...,xjMN is the number of samples of website behavior data to be clustered, M is the number of attributes contained in each website behavior data, and the attributes are referred to as dimensions hereinafter, where the attributes include, but are not limited to, user ID, device type, gender, age, time, location, duration, specific time, specific operation, and the like. It should be noted here that, in this embodiment, the website behavior data set is data obtained by a legal approach.
S102: the neighbors of each data are filtered to determine the filter window for the corresponding data, as shown in fig. 1.
In a specific implementation, for example, there are provided: and the number K of the neighbors is that K pieces of data are nearest to each data by using a K nearest neighbor method, the K pieces of data are neighbors of the data, and the filtering window of each data is determined by screening the neighbors of each data. k is a positive integer greater than or equal to 1.
The process of finding the nearest k pieces of data for each piece of data in the website behavior data set is as follows:
using Euclidean distance dij＝||xj-xi||2Calculating a distance matrix of the data;
the nearest k neighbors including itself are found for each data.
Specifically, the method for determining the filtering window comprises the following steps:
taking into account the data xkIs data xjNeighbor of (2), data xjNot necessarily data xkOf the network. It is therefore contemplated that the neighbors of each data point are filtered using subtraction (addition) to ensure that each data point and its neighbors are neighbors of each other. If data xkIs data xjNeighbor of (2), data xjNot data xkIs to filter x by subtractionkFrom xjDeleting the neighbor of (1), and performing addition screening by using xjIs added to xkIn the neighbourhood of (1);
each data point has its remaining neighbors with symmetry as a filtering window.
S103: randomly selecting a preset number of pieces of data from the website behavior data set as class center data respectively, and calculating the membership degree of each piece of data in the website behavior data set belonging to each class center data.
Presetting a clustering number C, randomly initializing C clustering centers, wherein the clustering centers select C pieces of data from website behavior data to be clustered as class center data respectively, each piece of data has M attributes, an iteration counter T is set to be 0, the maximum iteration number T is set to be 150, the weight of each dimension is set to be 1/M, and a stopping threshold xi of a fuzzy clustering algorithm is set to be 10-6。
S104: based on the filtering window of each data, each dimension of each data is used as a guide to filter the membership degree, and the weighted sum of the membership degrees after multidimensional filtering is used as the final membership degree after filtering, as shown in fig. 2.
Specifically, calculating the membership degree u of the jth data belonging to the ith clustering centerij(ii) a And filtering the membership degrees by taking each dimension of each datum in the website behavior data set as a guide, then weighting and summing the membership degrees after multidimensional filtering to be used as the final filtered membership degree, and then using the filtered membership degree for subsequent calculation.
As shown in fig. 3, the step of guiding filtering to filter the membership degree includes the following steps:
(1) dividing the obtained C multiplied by N membership degree matrix into C1 multiplied by N membership degree matrixes;
(2) using each dimension of the original data as guide data, and calculating the membership degree of each class according to a formula
Filtering, wherein u'ijmRepresenting the jth data of a warpMembership, u, of the ith class after m-dimensional filteringijRepresenting degree of membership, x, of the jth data belonging to the ith classjmValue, ω, representing the mth dimension of the jth pilot datakWindow representing the derivative data centered on the kth data, akmAnd bkmThe representation window omegakLinear coefficient of the m-th dimension, epsilon being the prevention of akmAn excessive pilot filter parameter, here typically 10-4Using the formula
And
find akmAnd bkmIn which μkmAnd
the mth dimension representing the guidance data is in the window ωkIs the window ωkThe amount of the data in (a) to (b),
is input membership uijAt window omegakIs measured.
Wherein the membership degree calculation formula is
In the formula wimIs the m-dimension attribute weight of the ith class, alpha is a fuzzy coefficient which is generally 2, xjmIs the value of the mth dimension of the jth data, vimIs the value of the mth dimension of the ith cluster center.
S105: and updating each class center data by using the finally filtered membership degree, and further updating the attribute weight of each dimension.
Updating the clustering center v of the ith class by combining the obtained filtered membership degreeimAnd using the obtained clustering center for subsequent calculation; updating the m-dimension attribute weight w of the i-th class by combining the obtained membership degree and the clustering centerim。
The cluster center is calculated by the formula
The multi-dimensionally filtered result is in accordance with
The weighted sum yields the final filtered membership, where wimThe weighting of the ith class and the mth dimension is expressed by two ways, one is mean weighting, i.e. the weighting of each dimension is 1/M, and the other is weight updating formula through EFWFCM
The weight obtained here is gamma (gamma)>0) Is a regularization scalar.
S106: and (4) judging the termination condition of the step of updating each class center data through iterative calculation, and finally outputting a website behavior classification result.
Wherein, the termination condition of the step of updating each class center data is as follows: and iteratively calculating the difference value between the two adjacent set objective function values to be smaller than a set value or the iteration times to exceed a set threshold value.
Calculating the objective function value F obtained by the t iteration(t)；
Calculating the value F of the objective function obtained by the t iteration(t)Value of objective function F for t-1 th iteration(t-1)The difference between if | | F (t) -F (t-1) | non-calculation<ξ or t>And T, terminating iteration and outputting a clustering result, if the iteration is not met, repeatedly executing the step S103 to the step S106 until the iteration termination condition is met and outputting the clustering result.
Wherein a formula is utilized
To calculate the objective function value F obtained from the t-th iteration(t)。
Example two
The embodiment provides a website behavior classification method, which specifically comprises the following modules:
the website behavior data acquisition module is used for acquiring a website behavior data set; wherein, one attribute of each data in the set is a dimension;
a filter window determination module for screening the neighbors of each data to determine a filter window for the corresponding data;
the class center data initialization module is used for randomly selecting a preset number of pieces of data from the website behavior data set as class center data respectively and calculating the membership degree of each piece of data in the website behavior data set belonging to each class center data;
the membership calculation module is used for filtering membership degrees based on a filtering window of each datum and using each dimension of each datum as a guide respectively, and weighting and summing the membership degrees after multidimensional filtering to be used as the final membership degree after filtering;
the attribute weight updating module is used for updating each class center data by utilizing the finally filtered membership degree so as to update the attribute weight of each dimensionality;
and the classification result output module is used for iteratively calculating and judging the termination condition of the step of updating each class center data and finally outputting the website behavior classification result.
It should be noted that, each module of the present embodiment corresponds to each step of the first embodiment one to one, and the specific implementation process is the same, which will not be described herein again.
EXAMPLE III
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in the website behavior classification method according to the first embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Example four
The embodiment provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps in the website behavior classification method according to the first embodiment are implemented.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A website behavior classification method is characterized by comprising the following steps:
acquiring a website behavior data set; wherein, one attribute of each data in the set is a dimension;
screening the neighbors of each data to determine a filtering window of the corresponding data;
randomly selecting a preset number of pieces of data from the website behavior data set as class center data respectively, and calculating the membership degree of each piece of data in the website behavior data set belonging to each class center data;
based on the filtering window of each data, each dimension of each data is used as a guide to filter the membership degree, and the weighted sum of the membership degrees after multi-dimensional filtering is used as the final membership degree after filtering;
updating each class center data by using the finally filtered membership degree, and further updating the attribute weight of each dimension;
and (4) judging the termination condition of the step of updating each class center data through iterative calculation, and finally outputting a website behavior classification result.
2. A method for website behavior classification as defined in claim 1, wherein each data in the set contains at least two attributes.
3. The website behavior classification method according to claim 1, wherein a K-nearest neighbor method is used to find K nearest pieces of data for each data in the website behavior data set, and the K pieces of data are neighbors of the corresponding data; k is a positive integer greater than or equal to 1.
4. The website behavior classification method according to claim 3, wherein the process of finding the nearest k pieces of data for each data in the website behavior data set is as follows:
calculating a distance matrix of the data using euclidean distances;
the nearest k neighbors including itself are found for each data.
5. The website behavior classification method according to claim 1, wherein the process of determining the filtering window of the corresponding data is:
screening the neighbors of each data point by using subtraction or addition to ensure that each data point and the neighbors thereof are neighbors;
each data point has its remaining neighbors with symmetry as a filtering window.
6. The website behavior classification method according to claim 1, wherein the formula for filtering the membership degree by using each dimension of each data as a guide is
Wherein u isi'jmMembership, u, of the ith class filtered by the mth dimension of the jth dataijRepresenting degree of membership, x, of the jth data belonging to the ith classjmValue, ω, representing the mth dimension of the jth pilot datakWindow representing the derivative data centered on the kth data, akmAnd bkmThe representation window omegakLinear coefficients in the m-th dimension.
7. The website behavior classification method according to claim 1, wherein the step of updating each of the class-centered data is terminated on a condition that: and iteratively calculating the difference value between the two adjacent set objective function values to be smaller than a set value or the iteration times to exceed a set threshold value.
8. A website behavior classification system, comprising:
the website behavior data acquisition module is used for acquiring a website behavior data set; wherein, one attribute of each data in the set is a dimension;
a filter window determination module for screening the neighbors of each data to determine a filter window for the corresponding data;
the class center data initialization module is used for randomly selecting a preset number of pieces of data from the website behavior data set as class center data respectively and calculating the membership degree of each piece of data in the website behavior data set belonging to each class center data;
the membership calculation module is used for filtering membership degrees based on a filtering window of each datum and using each dimension of each datum as a guide respectively, and weighting and summing the membership degrees after multidimensional filtering to be used as the final membership degree after filtering;
the attribute weight updating module is used for updating each class center data by utilizing the finally filtered membership degree so as to update the attribute weight of each dimensionality;
and the classification result output module is used for iteratively calculating and judging the termination condition of the step of updating each class center data and finally outputting the website behavior classification result.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the website behavior classification method according to any one of claims 1 to 7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the website behavior classification method according to any one of claims 1 to 7 when executing the program.
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