Abstract: Concerning the clustering inaccuracy in Web usage mining, an improved clustering algorithm based on relative Hamming distance and conflicting degree was given. In this algorithm, a URL-UserID associated matrix was set up, where URL and UserID of Web site were taken as row and column respectively, and each element's value of this matrix was the user's hits. Then, similar customer groups or relevant Web pages were obtained by measuring the similarity between column vectors or between row vectors of the associated matrix. The experiments show that the new algorithm is more accurate.

Key words: clustering algorithm, relative Hamming distance, conflicting degree, Web usage mining, network security

摘要： 针对Web使用挖掘中聚类结果准确性不高的问题,提出了一种改进的基于相对Hamming距离和类不一致度的聚类算法。该算法首先以Web站点的URL为行、以UserID为列建立关联矩阵,元素值为用户的访问次数;然后,对所建立关联矩阵的列向量或行向量进行相似性度量,获得相似客户群体或相关页面。实验表明,该算法具有较高的准确性。

关键词: 聚类算法, 相对Hamming距离, 不一致度, Web使用挖掘, 网络安全