A maximal frequent itemset approach for web document clustering

To efficiently and yet accurately cluster web documents is of great interests to web users and is a key component Of the searching accuracy of a web search engine. To achieve this, this paper introduces a new approach for the clustering of web documents, which is called Maximal Frequent Item-set(MFI) approach. Iterative clustering algorithms, such as K-means and Expectation-Maximization(EM), are sensitive to their initial conditions. MFI approach firstly locates the center points of high density clusters precisely. These center points then are used as initial points for the K-means algorithm. Our experimental results tested on 3 web document sets show that our MFI approach outperforms the other methods we compared in most cases, particularly in the case of large number of categories in web document sets.