A new method for Web users fuzzy clustering based on analysis of user interest characteristic is proposed in this article. The method first defines page fuzzy categories according to the links on the index page of the...A new method for Web users fuzzy clustering based on analysis of user interest characteristic is proposed in this article. The method first defines page fuzzy categories according to the links on the index page of the site, then computes fuzzy degree of cross page through aggregating on data of Web log. After that, by using fuzzy comprehensive evaluation method, the method constructs user interest vectors according to page viewing times and frequency of hits, and derives the fuzzy similarity matrix from the interest vectors for the Web users. Finally, it gets the clustering result through the fuzzy clustering method. The experimental results show the effectiveness of the method.展开更多
We propose an efficient hybrid algorithm WDHP in this paper for mining frequent access patterns. WDHP adopts the techniques of DHP to optimize its performance, which is using hash table to filter candidate set and tri...We propose an efficient hybrid algorithm WDHP in this paper for mining frequent access patterns. WDHP adopts the techniques of DHP to optimize its performance, which is using hash table to filter candidate set and trimming database. Whenever the database is trimmed to a size less than a specified threshold, the algorithm puts the database into main memory by constructing a tree, and finds frequent patterns on the tree. The experiment shows that WDHP outperform algorithm DHP and main memory based algorithm WAP in execution efficiency.展开更多
文摘A new method for Web users fuzzy clustering based on analysis of user interest characteristic is proposed in this article. The method first defines page fuzzy categories according to the links on the index page of the site, then computes fuzzy degree of cross page through aggregating on data of Web log. After that, by using fuzzy comprehensive evaluation method, the method constructs user interest vectors according to page viewing times and frequency of hits, and derives the fuzzy similarity matrix from the interest vectors for the Web users. Finally, it gets the clustering result through the fuzzy clustering method. The experimental results show the effectiveness of the method.
文摘We propose an efficient hybrid algorithm WDHP in this paper for mining frequent access patterns. WDHP adopts the techniques of DHP to optimize its performance, which is using hash table to filter candidate set and trimming database. Whenever the database is trimmed to a size less than a specified threshold, the algorithm puts the database into main memory by constructing a tree, and finds frequent patterns on the tree. The experiment shows that WDHP outperform algorithm DHP and main memory based algorithm WAP in execution efficiency.
