Abstract: In this paper，a method of Web document clustering based on granular computing（WDCGrc） is presented.The method computes the weight value of the words in documents by adopting the TF-IDF principle.Meanwhile，combinative ways defining documents threshold and average weight value are adopted to reduce dimensions and extract the keywords in each document.The paper establishes the transformation between the keywords in documents and the binary granules，and adopts the algorithm of association rules based on granular computing to obtain frequent itemsets between documents.These frequent itemsets form initial clusters.The initial clusters are optimized through the algorithm of optimization and the clustering result is obtained.The experiment shows that the method is practical and feasible，with good quality of clustering.

Key words: granular computing, clustering, association rules, Web documents

摘要： 提出了一种基于粒计算Web文档聚类（WDCGrc）方法。该方法通过TF-IDF法则计算文档词条的权值，采取设定文档阈值和平均权值相结合的方法实行降维，抽取出每篇文档的主干词；建立了文档的主干词和二进制粒之间的转换，提出了基于粒计算提取文档间的关联规则算法来获取文档间的频繁项集，由频繁项集形成初始聚类，使用优化算法对初始聚类进行优化，得到最终聚类结果。实验结果表明，该方法切实有效，聚类质量较好。

关键词: 粒计算, 聚类, 关联规则, Web文档