一种新的Web用户行为模式挖掘算法的研究

从Web日志文件中挖掘出用户行为模式，是所有Web站点管理者的迫切需要，但由于web日志数据量大，存有大量的干扰和不完整的数据，导致无法准确的抽取出用户行为的模式。小环境无监督聚类算法适合挖掘具有噪音和不完整数据的大量数据集，但它是基于欧几里德空间的二维模型，数据表示不直观。我们对UNC进行改进，提出了具有层次结构的UNC(简称LUNC)。性能测试实验证明，该模型具有较好的整体性能。

Evolutionary Approach to Mining Web User Profiles

A technique for mining Web session profile from the historic user access data stored in Web server logs is presented. A new level clustering technique that exploits the symbiosis between clusters in feature space and genetic biological niches in nature,called Level Unsupervised Niche Clustering(L-UNC) is presented. We use L-UNC as part of a complete system of knowledge discovery in Web usage data. Our experiments show that our algorithm is capable of extracting meaningful user profiles on real Web sites.