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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >Web text data mining method based on Bayesian network with fuzzy algorithms
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Web text data mining method based on Bayesian network with fuzzy algorithms

机译：基于模糊算法的贝叶斯网络的网络文本数据挖掘方法

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摘要

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.
机译：随着Web 3.0时代的出现，贝叶斯网络网页的数量和复杂性表现出爆炸性的增长趋势。伴随这是网页中包含的信息的几何生长。贝叶斯网络中的网络文本数据通常隐藏丰富的知识和用户价值规则。但是，由于网络文本数据的半结构化，实时和离散特性，用户难以从这些复杂的数据集直接获取所需的知识。模糊数学的出现提供了解决这些问题的良好研究理念和方法。它可以使用模糊数学的想法来分析文本数据中的实际问题。因此，如何有效地挖掘网络文本数据信息和知识，用户真正关心贝叶斯网络，并以用户可以理解的方式展示它，它目前是一个非常受欢迎的研究主题。在本文中，我们选择贝叶斯网络的文本：微博数据进行实验。通过使用模糊理论的相关知识建立了微博的用户数据模型。引入模糊措施的概念来计算检测指标之间的相互作用关系下的非加性测量值。确定检测用户和文本数据之间的成员资格函数关系，计算在非附加措施下的成员功能的Choquet积分，Sugeno积分和Wang积分的积分值，最终有价值的网络文本数据被积分值判断。在上述研究内容的基础上，贝叶斯网络社区的组合，设计和实施信息获取和分析，对Web文本挖掘技术和模糊算术数学的研究结果。本文实验方法获得的召回率低至4％，趋于更稳定。

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