基于粗糙集和模糊聚类的网站日志数据挖掘实例分析

Case Analysis of Web Log Data Mining Based on Rough Sets and Fuzzy Clustering

粗糙集理论是有效处理不精确、不确定和含糊信息的软计算工具,模糊聚类分析是依据客观事务间的特征、亲疏程度和相似性,通过建立模糊相似关系对客观事务进行分类的方法。网站日志数据通常是大量的,冗余的,日志中的页面之间的关系也是模糊的,不确定的。该文利用粗糙集理论和模糊聚类的方法对某学校网站的日志数据进行实例分析,抽取用户感兴趣的模式,理解用户的浏览兴趣行为,以便进一步改善网站结构,为用户提供个性化服务。

Rough Sets theory is a soft computing tool dealing with vague, imprecise, uncertain and incomplete data. And Fuzzy Clustering Analysis is an analysis method of object through establishing fuzzy analogical relations based on character, distance and similarity among objects. Web log data is generally redundant. Moreover the relations among the Web pages are fuzzy and uncertain. In this paper, one of the Web log data is analyzed based on Rough Sets and Fuzzy Clustering, and the interesting pattern from the log of websites is got, the user~ browse interest behavior is appended, so as to improve the website＇s structure and provide individual services for the LlSers.