Affiliations: [a] School of Public Affairs & Law, Asian Political Culture and Public Governance Research Center, Southwest Jiaotong University, Chengdu, Sichuan, China
| [b] 4th RD, Southwest China Institute of Electronic Technology, Chengdu, Sichuan, China
Correspondence:
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Corresponding author. Wei Zhao, School of Public Affairs & Law, Asian Political Culture and Public Governance Research Center, Southwest Jiaotong University, Chengdu 610031, Sichuan, China. E-mail: [email protected].
Abstract: With the advent of Web 3.0 era, the number and complexity of Web pages in Bayesian networks have shown an explosive growth trend. Accompanying this is the geometric growth of information contained in Web pages. Web text data in Bayesian networks usually hide rich knowledge and rules of user value. However, due to the semi-structured, real-time and discrete characteristics of Web text data, it is difficult for users to obtain the knowledge they need directly from such complex data sets. The emergence of fuzzy mathematics provides a good research idea and method for solving such problems. It can use the idea of fuzzy mathematics to analyze the practical problems in text data. Therefore, how to effectively mine the Web text data information and knowledge that users really care about from Bayesian network, and present it in a way that users can understand, it is a very popular research topic at present. In this paper, we select the text of Bayesian network: microblog data for experiments. User data model of microblog is established by using relevant knowledge of fuzzy theory. The concept of fuzzy measure is introduced to calculate the non-additive measure value under the interaction relationship between the detection indicators. Determine the membership function relationship between the detection user and the text data, calculate the integral values of Choquet integral, Sugeno integral and Wang integral of the membership function under the non-additive measure, the final valuable web text data is judged by integral value. On the basis of the above research contents, the research results of Web text mining technology and fuzzy arithmetic mathematics are combined, design and implement information acquisition and analysis for Bayesian network community. The recall rate obtained by the experimental method in this paper is as low as 4%, and tends to be more stable.
Keywords: Fuzzy algorithm, Bayesian network, data mining, web text data mining
