Social network user interest association rule mining method based on community division

A social network and user interest technology, which is applied in the field of social network user interest association rule mining based on community division, can solve the problems of large time consumption, lack of control, and many irrelevant interests, and achieve the goal of reducing data size and time consumption Effect

Inactive Publication Date: 2014-06-04
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The present invention provides a community-divided social network user interest association rule mining method for the shortcomings of tra...

Method used

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  • Social network user interest association rule mining method based on community division
  • Social network user interest association rule mining method based on community division
  • Social network user interest association rule mining method based on community division

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Embodiment Construction

[0044] A specific embodiment of the community discovery algorithm proposed by the present invention is given below, which is based on a hypothetical social network. Suppose there is such a social network: there are 8 users, namely A, B, C, D, E, F, G, and H, where A has friendship with B, C, and D, and B has friendship with A, C, and D. Friend relationship, C has a friend relationship with A, B, and D, D has a friend relationship with A, B, C, and E, E has a friend relationship with D, F, G, and H, and F has a friend relationship with E, G, and H , G has a friend relationship with E, F, and H, and H has a friend relationship with E, F, and G.

[0045]Phase 1: Correspond users in the social network to nodes in the network, and correspond A, B, C, D, E, F, G, and H to 1, 2, 3, 4, 5, 6, 7, and 8, respectively, A node set V={1, 2, 3, 4, 5, 6, 7, 8} is obtained. The edges in the network are generated according to the friendship between nodes, and the corresponding network can be ...

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Abstract

The invention provides a social network user interest association rule mining method based on community division. The method includes the steps that firstly, classification preprocessing is performed on data based on similarity, so that records in the same classification are high in similarity; secondly, association mining is performed on data of each classification to obtain frequent item sets of all the classifications; finally, all the frequent item sets of all the classifications are combined, and candidate association rules with the confidence coefficient larger than a confidence coefficient threshold value are selected to generate a final association rule collection. Generated unnecessary candidate item sets low in association performance can be reduced, so that the association rule mining efficiency of overall data is improved, and good expansibility is achieved.

Description

technical field [0001] The design goal of the present invention is to be able to mine the association rules among the interests of social network users. There are a large number of users in the social network, and the interests of each user are different, and the things they pay attention to are also different. The purpose is to mine the association rules between the interests of different users. This method is a simple but practical rule for data mining. , association rule mining is an important research topic of data mining. Background technique [0002] In the knowledge model of data mining, association rules are an important one. Association rule is a simple but applicable rule in data. The association rule mode is a descriptive mode, and the algorithm for discovering association rules is an unsupervised learning method. Traditional association rule mining algorithms are generally used to discover the rules followed by most data in the database, but at the same time th...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/903
Inventor 张小松牛伟纳罗强蒲福连张凡张蕾陈瑞东王东李宏鸢
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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