CN111666519A - Dynamic mining method and system for network access behavior feature group under enhanced condition - Google Patents
Dynamic mining method and system for network access behavior feature group under enhanced condition Download PDFInfo
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Abstract
The invention belongs to the field of data mining, and particularly relates to a dynamic mining method and a dynamic mining system for network access behavior feature groups under an enhanced condition. The scheme is as follows: providing an input interface for a user, inputting effective frequency statistical data of each webpage accessed by an individual by the user, converting the effective frequency statistical data into a matrix form, performing one-time scanning search on the basis of the matrix to search all maximum two clusters in the matrix and storing the maximum two clusters in a memory, then providing an interface for inputting matrix point or edge added data for the user, normalizing the added data input by the user into edge added data, finally performing an iterative search process on each piece of data, and outputting all maximum two clusters obtained by the last iteration, namely all clusters with the maximized common network access behavior characteristic.
Description
Technical Field
The invention belongs to the field of data mining, and particularly relates to a dynamic mining method and system for network access behavior feature groups under an enhanced condition.
Background
At present, the relationship graph is widely applied in the scientific fields such as social networks, gene biology, cognitive radio and the like. In many large data fields, there is a need to search for populations or targets with maximized common characteristics. The groups or targets and their features are usually abstractly expressed in the form of various graphs, wherein the groups or targets with maximized common features are expressed in the form of some special graphs, including: maximum clique, maximum bipartite clique, quasi-bipartite clique, maximum edge bipartite clique, maximum balance bipartite clique, and frequent item set, among others.
The invention mainly aims at online network access relations, and searches a group with a maximized common access relation from the online network access relations, wherein the group with the maximized common access relation is essentially the largest binary group, and the invention mainly relates to an iterative largest binary group searching technology. It has been proven that the maximum two-cluster search problem is equivalent to the maximum frequent closed item set search problem, so in recent years, the maximum two-cluster search technology has been rapidly developed in the fields of various databases and relational maps, and the main algorithms include: LCM algorithm, DCI-CLOSED algorithm, DataPeelr algorithm, LCM-MBC algorithm, and CubeMiner-MBC algorithm. The LCM algorithm has the main idea that the frequent item sets are listed, and unnecessary frequent item sets are reduced by utilizing pruning. The DCI-CLOSED algorithm focuses on enumerating the largest bipartite blob from a large bipartite graph. The DataPeeler algorithm efficiently mines the closed frequent item sets corresponding to the largest two clusters one by one from the three-dimensional data set. The LCM-MBC algorithm searches the symmetric undirected large graph for the largest biblob with a mining vertex value greater than a threshold value. The CubeMiner-MBC algorithm adopts a subtree pruning technology based on the symmetry of a graph data set, and enumerates the 3D maximum binary clusters from a 3D symmetric matrix by using the symmetry enumeration of the graph. The EMBE algorithm searches the maximum two clusters with limited characteristics by using a dynamic threshold, and can output all the maximum two clusters under the condition of no limitation, and the efficiency is slightly higher than that of the LCM-MBC algorithm. The above algorithm searches for the largest blob if the input data remains static. However, in many application scenarios, when the external environment changes, the input data also changes, including the situation where the edges or vertices of the graph increase or increase.
Aiming at the scene that the input data can be dynamically changed, the maximum two clusters are searched in the dynamically changed data mainly by adopting a method based on a sliding window at present, and the main algorithms comprise algorithms such as a Max-FISM algorithm, a VSW algorithm, an AFIM algorithm, a GGACFI-MFW algorithm and the like. Wherein the Max-FISM algorithm mines a frequent set of items in a sliding window of a continuous data stream. The VSW algorithm may continuously mine frequent patterns over a sliding window of variable size. The AFIM algorithm uses a sliding window model plus limited fault tolerance capabilities to search a frequently closed set of items. The GGACFI-MFW algorithm efficiently mines an approximate set of frequently closed items throughout the entire online data stream based on a maximum frequency window model. Although these methods are capable of searching for the largest blobs in dynamically changing data, such sliding window based methods are inherently limited by the size of the window, and the results tend to be coarse rather than precise.
In the dynamic change process of input data, two conditions of data degradation and enhancement are included: data degradation refers to the situation where a point or edge in the input data disappears; data enhancement refers to the situation where points or edges in the input data are increased. The two different types of dynamic changes and the searching techniques thereof are completely different, the invention focuses on the situation that the second point or edge is increased, and provides a dynamic mining method for accurately and efficiently searching the network access behavior characteristic group under the enhanced condition.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a dynamic mining method and a dynamic mining system for network access behavior feature groups under an enhanced condition, aiming at statistical data of an individual access webpage, under the continuous change condition that the individual (namely point) and the access relation (namely edge) are increased, an intelligent model capable of quickly and efficiently searching the maximum two clusters in the changed data is established, all groups with the maximized common access feature are determined, and a user can accurately and quickly lock, track or monitor a target group.
The technical scheme adopted by the invention is as follows:
a dynamic mining method for network access behavior feature groups under an enhanced condition comprises the following steps:
providing an input interface for a user, and inputting effective frequency statistical data of each type of webpage accessed by an individual by the user;
converting the effective frequency statistical data into a 0,1 frequency matrix, and performing a scanning search on the basis of the frequency matrix to obtain all maximum binary groups in the frequency matrix and storing the maximum binary groups in a memory;
providing an interface for inputting frequency matrix points or edge increasing data for a user, and normalizing the increasing data input by the user into edge increasing data;
an iterative search process is performed on each incremental datum and all the largest blobs obtained last time, i.e., the population having the characteristics of maximizing common network access behavior, are output.
According to the dynamic population mining method for the network access behavior characteristics under the enhanced condition, a user inputs effective frequency statistical data of each type of webpage accessed by an individual through the input interface, the individual is an internet user, the effective frequency statistical data refers to the time of day as a unit between the time of accessing the type of webpage for the first time and the current time divided by the total number of times of accessing the type of webpage by the individual, the effective frequency statistical data of accessing the type of webpage by the individual is finally normalized to be 0 or 1, wherein 0 represents that the frequency is insufficient, and 1 represents that the frequency is sufficient.
In the method for dynamically mining the network access behavior feature group under the enhanced condition, converting the frequency statistical data into a 0,1 matrix means that the frequency statistical data input by the user is processed and expressed as a matrix, wherein one row of the matrix represents an individual, one column of the matrix represents a type of web page, and elements of the matrix represent the access frequency of the individual to the corresponding type of web page.
In the method for dynamically mining the network access behavior feature population under the enhanced condition, the EMBE algorithm is searched by performing one-time scanning on the basis of the matrix, all the maximum two clusters in the matrix are obtained and stored in the memory, the step of performing RIMBE algorithm iterative search on the converted matrix to obtain all the maximum two clusters, and one maximum two cluster represents the most users with the same access webpage types.
In the method for dynamically mining the network access behavior feature group under the enhanced condition, an interface for inputting matrix points or increasing data is provided for the user, wherein the point increasing data interface refers to which individuals represented in the user input matrix are increased, and the increasing data interface refers to which individuals represented in the user input matrix have the frequency of accessing the webpage changed from 0 to 1.
In the method for dynamically mining the network access behavior feature group under the enhanced condition, the normalization of the added data input by the user into the added data of the edges refers to an increase condition of converting the added individual input by the user into a plurality of edges, for example, if the user adds one individual, the method is equivalent to completely increasing all access frequency data corresponding to the individual, and finally converting all the added point or edge conditions input by the user into the increase condition of the plurality of edges.
The method for dynamically mining the network access behavior feature population under the enhanced condition includes executing a maximum clustering iterative search process on each added data, and outputting all maximum clustering obtained by the last iteration, where executing an iterative search process on the basis of the maximum clustering obtained by the first search, that is, deciding the maximum clustering obtained by each search on each added edge, and if the maximum clustering includes the added edge, obtaining and judging, where obtaining refers to decomposing the maximum clustering with the added edge into left and right clustering according to the added edge, and judging whether the two clustering remain as the maximum clustering, and if the decomposed result is the maximum clustering, storing the maximum clustering obtained by decomposition. Each time an added edge is processed, a new set of maximum binary clusters is obtained, and when the next added edge is processed, the above processing procedure is repeated based on the newly obtained maximum binary clusters.
A dynamic group mining system for network access behavior characteristics under an enhanced condition comprises a user data input interface module, a data-matrix conversion module, an EMBE search module, an input interface module for adding data to points or edges, a normalization edge processing module and an iteration search module; the user data input interface module is used for inputting effective frequency statistical data of each type of webpage accessed by an individual; the data-matrix conversion module converts effective frequency statistical data input by a user into a 0,1 matrix; the EMBE searching module performs one-time scanning search on the 0,1 matrix according to the EMBE searching method, and acquires and stores all maximum two clusters in the matrix; the input interface module of the point or edge added data is used for inputting points or edges needing to be added in the matrix; the normalization edge processing module converts all the increasing points or edge conditions input by the user into the increasing conditions of a plurality of edges and records the increasing conditions; and the iterative search module sequentially processes the condition of each added edge, performs next search processing on the basis of the processing result of the previous edge, and outputs all maximum two clusters obtained by the last iteration, namely the cluster with the maximized common network access behavior characteristic.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, all maximum groups can be searched in the changed data quickly and efficiently under the continuous change condition that the individual (point) and the access relation (edge) are increased for the statistical data of the individual access webpage, all groups with the maximized common access characteristic are determined, and a user can lock, track or monitor the target group accurately and quickly. Compared with the prior art that the specific population cannot be quickly and accurately searched, the invention provides an iterative search (RIMBE) method, and for dynamically changed data, only the changed data need to be searched, and the whole data does not need to be searched, so that the specific population can be quickly and accurately searched.
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FIG. 1 is a flow chart of the scheme of the invention.
Detailed Description
Embodiments of the present invention are further provided in the following description with reference to the drawings.
The method of the invention develops a prototype system, which comprises a user data input interface, a data-matrix conversion module, an EMBE (empirical algorithm for maximum biclicity execution) searching module, an input interface for adding data on points or edges, a normalized edge processing module and an iterative search (RIMBE) module: inputting effective frequency statistical data of each type of webpage accessed by an individual through a data input interface by a user; the data-matrix conversion module converts effective frequency statistical data input by a user into a 0,1 matrix; the EMBE searching module performs one-time scanning on the matrix according to the EMBE searching method to search and store all the maximum two subgroups in the matrix; inputting points or edges needing to be added in the matrix through an input interface for adding data by a user; the normalization edge processing module is used for converting all the increasing points or edge conditions input by the user into the increasing conditions of a plurality of edges and recording the increasing conditions; and the iterative search (RIMBE) module sequentially processes the condition of each added edge and carries out the next search processing on the basis of the processing result of the previous edge.
As shown in fig. 1, the specific operation process of the present invention is as follows:
(1) the user inputs effective frequency statistical data of each type of webpage accessed by an individual through the input interface, the individual is an internet user, the effective frequency statistical data refers to the total times of the individual accessing a certain type of webpage divided by the time of the individual accessing the certain type of webpage to the current time in days, and the user determines that the effective frequency is 0 or 1.
(2) The system converts the effective frequency statistic data input by the user into a 0,1 matrix through the data-matrix conversion module, namely, the effective frequency statistic data input by the user is processed and expressed into a matrix M, wherein one row of the matrix represents an individual, one column of the matrix represents a type of webpage, and elements of the matrix represent the access frequency of the individual to the webpage of the corresponding type. An example of the matrix is shown in table 1, which contains effective frequency data of five types of web pages, i.e., 0,1,2, 3, and 4, accessed by five individuals (internet users), a, b, c, d, and e.
TABLE 1
| 0 | 1 | 2 | 3 | 4 | |
| a | 0 | 1 | 0 | 1 | 1 |
| b | 1 | 0 | 1 | 1 | 1 |
| c | 0 | 1 | 0 | 1 | 1 |
| d | 1 | 1 | 1 | 0 | 0 |
| e | 1 | 1 | 1 | 0 | 0 |
(3) The system uses The EMBE search module to scan and search all The maximum dices in The matrix M according to The existing EMBE search method (Qin C X, Liao MX, Liang Y, et al. effective Algorithm for maximum binary implementation on binary Graphs [ C ]// The International Conference on Natural Computation, Fuzzy Systems and Knowledge discovery. spring, Cham,2019:3-13.) and store The maximum dices in B. For example, by searching the matrix represented by table 1 according to the EMBE method, the largest bipartite group (i.e., the most users with the most same visited web page types) can be obtained as { (a, c) - (1,3,4), (b, d, e) - (0,2), (a, b, c) - (3,4), (d, e) - (0,1,2), (a, c, d, e) -1, (b) - (0,2,3,4) }.
(4) The system adds data input interface through the point or edge, which means that the user inputs the point or edge to be added in the M. For example, for Table 1, the user may add point f or add edge c-2.
(5) The system converts all the added points or edge conditions input by the user into the added conditions of a plurality of edges through the normalization edge processing module, and records the added conditions into E. As shown in Table 1, when the user adds edge f-2, the system will automatically convert this addition to adding all edges corresponding to point f, i.e., adding the edge of f-2 at the same time.
(6) The system executes a search through the iterative search (RIMBE) module, specifically an iterative search according to the following procedure.
(6.1) set B' to null.
(6.2) removing an edge E ═ from E<v1,v2>. Wherein v is1One row V from the representative matrix1，v2One column V from the representative matrix2E represents matrix and V1Rows and V2The associated corresponding elements are listed.
(6.3) taking out a maximum micelle G from B.
(6.4) if G does not contain e, then put G to B'; if G contains e, then decompose G into left sub-graph G1And G2Left drawing G1＝G+v1Right subfigure G2＝G+v2If G is1Is the largest two clusters, G is1Put into B', if G2Is the largest two clusters, G is2Placing into B'; otherwise, G is put to B'.
(6.5) if G is the last maximum two blobs, putting the maximum two blobs in B 'into B, namely B ← B', otherwise, directly returning (6.3).
(6) And outputting a set B'.
The effectiveness comparison is carried out by using an EMBE algorithm repeated search method and the iterative search method RIMBE, the search efficiency comparison results under the conditions of different matrixes with different sizes and different matrix densities are shown in the table 2, and the results show that the method has high efficiency on the premise of keeping accuracy, and the search time is far shorter than that of the repeated search method.
TABLE 2
Aiming at the statistical data of the individual access webpage, the method can accurately and efficiently search the maximum binary group in the changed data under the continuous change condition that the individual (namely point) and the access relation (namely edge) are increased, and determine all groups with the maximized common access characteristic.
Based on the same inventive concept, another embodiment of the present invention provides an electronic device (computer, server, smartphone, etc.) comprising a memory storing a computer program configured to be executed by the processor and a processor, the computer program comprising instructions for performing the steps of the inventive method.
Based on the same inventive concept, another embodiment of the present invention provides a computer-readable storage medium (e.g., ROM/RAM, magnetic disk, optical disk) storing a computer program, which when executed by a computer, performs the steps of the inventive method.
The invention has not been described in detail and is part of the common general knowledge of a person skilled in the art.
The foregoing disclosure of the specific embodiments of the present invention and the accompanying drawings is directed to an understanding of the present invention and its implementation, and it will be appreciated by those skilled in the art that various alternatives, modifications, and variations may be made without departing from the spirit and scope of the invention. The present invention should not be limited to the disclosure of the embodiments and drawings in the specification, and the scope of the present invention is defined by the scope of the claims.
Claims (10)
1. A dynamic mining method for network access behavior feature groups under an enhanced condition is characterized by comprising the following steps:
receiving effective frequency statistical data of each type of webpage accessed by an individual, which is input by a user;
converting the effective frequency statistical data into a 0,1 frequency matrix, and performing one-time scanning search on the basis of the frequency matrix to obtain all maximum binary groups in the frequency matrix;
receiving frequency matrix point increasing data or edge increasing data input by a user, and normalizing the increasing data input by the user into edge increasing data;
and performing an iterative search process on each piece of added data, and outputting all maximum groups obtained by the last iteration, namely the group with the characteristics of the maximized common network access behaviors.
2. The method as claimed in claim 1, wherein the individual is a web user, the effective frequency statistics data is obtained by dividing the total number of times that the individual accesses a certain type of web page by the time in days between the time of accessing the certain type of web page for the first time and the current time, and the effective frequency statistics data of the certain individual accessing a certain type of web page is finally normalized to 0 or 1, where 0 represents insufficient frequency and 1 represents sufficient frequency.
3. The method of claim 1, wherein the step of converting the frequency statistics into a 0,1 matrix comprises: and processing the effective frequency statistical data input by the user, and expressing the effective frequency statistical data as a matrix, wherein one row of the matrix represents an individual, one column of the matrix represents a type of webpage, and the elements of the matrix represent the access frequency of the individual to the corresponding type of webpage.
4. The method according to claim 1, wherein the point increment data is used to determine which individuals in the frequency matrix are increased, and the edge increment data interface is used to determine which individuals in the frequency matrix access the web page with a frequency of 0 to 1.
5. The method according to claim 1, wherein the normalizing the incremental data input by the user into the incremental data of the edges includes converting the added individuals input by the user into a plurality of edges, the adding of one individual by the user is equivalent to adding all access frequency data corresponding to the individual, and the adding of all points or edges input by the end user is converted into the adding of the plurality of edges.
6. The method for dynamically mining the network access behavior feature population under the enhanced condition according to claim 1, wherein the step of performing an iterative search process on each piece of added data comprises the following steps: for each added edgee＝{v1,v2In which v is1One row V from the representative matrix1，v2One column V from the representative matrix2E represents matrix and V1Rows and V2The corresponding elements associated with the columns are used for deciding each maximum two-clustering G in the set B respectively; if G does not contain e, the set B is not changed, otherwise, G is taken out of B, the acquisition and judgment method is adopted to carry out acquisition and judgment, the maximum two clusters obtained by the acquisition and judgment are added into the set B, the set B is updated, and the iteration is carried out until all the added edges are processed.
7. The method for dynamically mining the network access behavior feature population under the enhanced condition according to claim 6, wherein the obtaining and judging are performed by adopting an obtaining-judging method, and the step of adding the maximum two clusters obtained by obtaining and judging into the set B comprises the following steps: for the case that the added edge e is included ═ v1,v2The largest two clusters G of the graph are taken out and decomposed into a left subgraph G1And the right subfigure G2Two sub-graphs, left sub-graph G1＝G+v1Right subfigure G2＝G+v2Finally, respectively determine G1And G2If G is1At maximum two clusters, G is added1Added to set B if G2At maximum two clusters, G is added2Add to set B.
8. A dynamic group mining system for network access behavior characteristics under an enhanced condition is characterized by comprising a user data input interface module, a data-matrix conversion module, an EMBE (empirical mode decomposition) search module, an input interface module for adding data to points or edges, a normalization edge processing module and an iteration search module; the user data input interface module is used for inputting effective frequency statistical data of each type of webpage accessed by an individual; the data-matrix conversion module converts effective frequency statistical data input by a user into a 0,1 matrix; the EMBE searching module performs one-time scanning search on the 0,1 matrix according to the EMBE searching method, and acquires and stores all maximum two clusters in the matrix; the input interface module of the point or edge added data is used for inputting points or edges needing to be added in the matrix; the normalization edge processing module converts all the increasing points or edge conditions input by the user into the increasing conditions of a plurality of edges and records the increasing conditions; and the iterative search module sequentially processes the condition of each added edge, performs next search processing on the basis of the processing result of the previous edge, and outputs all maximum two clusters obtained by the last iteration, namely the cluster with the maximized common network access behavior characteristic.
9. An electronic apparatus, comprising a memory and a processor, the memory storing a computer program configured to be executed by the processor, the computer program comprising instructions for performing the method of any of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a computer, implements the method of any one of claims 1 to 7.
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