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Word clustering is important for automatic thesaurus construction, text classification, and word sense disambiguation. Recently, several studies have reported using the web as a corpus. This paper proposes an unsupervised algorithm for word clustering based on a word similarity measure by web counts. Each pair of words is queried to a search engine, which produces a co-occurrence matrix. By calculating the similarity of words, a word co-occurrence graph is obtained. A new kind of graph clustering algorithm called Newman clustering is applied for efficiently identifying word clusters. Evaluations are made on two sets of word groups derived from a web directory and WordNet.