Abstract
The number of web pages in Internet is increased by day by. New techniques are developed to reach or retrieve information from the documents in those web pages. Clustering is one of techniques used on web documents. In this study, the techniques such as Euclidean, Cosine, Pearson and Extended Jaccard used to find document similarities in web pages were tested by two data sets and performances were studied. In the experiments done for web documents clustering, found that Euclidean distance measure has high fault rates. The best performance in the similarity measures are provided by Cosine and Extended Jaccard measures. According to results of experiments that Cosine similarity measure was found suitable to use in the web documents clustering.
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