基于归纳化会话的网络用户的聚类

为了发掘具有相似的访问兴趣的网络用户，探讨了网络用户聚类的问题。网络用户的访问信息从服务器日志文件中抽取出来，组织成会话向量的形式，会话描述为一段时间内用户向服务器发出一系列访问请求。为了减少会话向量的维度，根据网页的层次性，采用面向属性的推理方法，对这些会话进行了归纳，并且定义了一个新的距离测度来描述两个会话之间的相似度，最后采用某种非欧几里德的关系聚类算法聚类这些归纳化的会话。实验表明，这种方法对在大型的日志文件集中挖掘出有意义的网络用户的分类是高效可行的。

CLUSTERING OF WEB USERS BASED ON THE GENERALIZED SESSIONS

In order to find Web users with similar access interest, clustering of Web users is studied in this paper. Access records of the Web users are extracted from Web severs' log files and organized into user sessions. Each session is a compact sequence of Web accesses by a user. Using attributed oriented induction, the sessions are then generalized according to the page hierarchy. A new distance is defined to measure similarity between two generalized sessions. The generalized sessions are finally clustered using some non Euclidean and relational clustering algorithms. The experiment shows that this approach is efficient and practical in finding several interesting clusters within a large set of log.