一种基于集合的Web用户会话实时聚类算法

随着互联网的普及和电子商务、个性化推荐技术等的发展,Web使用挖掘成为了数据挖掘的新的研究热点。针对Web用户会话聚类,提出了一种基于序列对集合的用户会话实时聚类方法。对聚类算法进行了分析与比较,给出了时空复杂度,实验比较了BOM算法与BOC算法的效率,并验证了BOC算法的有效性与时效性。

Web User Session Realtime Clustering Algorithm Based on Set

With the popularity of the internet and the development of the e-commerce and personalized recommendation technology,web usage mining becomes the new study hot of data mining.For web user session clustering,a user session realtime clustering method that based on sequence-pair set is putted forward.The clustering algorithm is analyzed and compared,the space-time complexity is given.By comparing to the efficiency of the BOM algorithm and BOC algorithm,the effectiveness and timeliness of the BOC algorithm are tested.