Improving Web Clustering by Cluster Selection
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
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Web Clustering is useful for several activities in the WWW, from automatically building web directories to improve retrieval performance. Nevertheless, due to the huge size of the web, a linear mechanism must be employed to cluster web documents. The k-means is one classic algorithm used in this problem. We present a variant of the vector model to be used with the k-means algorithm. Our representation uses symbolic objects for clustering web documents. Some experiments were done with positive results and future work is optimistic.