关键词: web挖掘, 模糊粗糙聚类, web访问模式, 夹角余弦, 并行

Abstract: Web log mining analyzes user access patterns to gain users’ level of interest. Currently, most web log mining is based on frequency, but the information that it mines is not of much value. In this paper, the proposed clustering technique is based on access time, firstly, the fuzzy vector is used to represent the user access patterns, recording whether the user has visited the page and the time of browsing. Then, the users’ access sequences are analyzed by different clustering methods. In addition, a two-layer clustering technique is proposed based on the fuzzy rough [k]-means and angle cosine, which can reduce the sensitivity to the initial clustering center. And the feasibility of the clustering method is demonstrated by a series of experiments. The results of different clustering methods are verified by using the Davies-Bouldin index. When the data sets are too large, the algorithm is inefficient. Therefore, it uses MapReduce to realize the parallelism of two-layer clustering, improving the efficiency of clustering.

Key words: web mining, fuzzy rough clustering, web access patterns, angle cosine, parallel