Cure: Clustering on Sequential Data for Web Personalization: Tests and Experimental Results
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CURE: CLUSTERING ON SEQUENTIAL DATA FOR WEB PERSONALIZATION: TESTS AND EXPERIMENTAL RESULTS K. Santhisree, and A. Damodaram Department of Computer Science, Jawaharlal Nehru Technological University, Hyderabad E-mail: [email protected], E-mail: [email protected] The world wide web is full of multi-disciplinary data for knowledge data discovery research. In this paper we present CURE (Clustering usage Representatives) algorithm to find clusters on a web usage data. We adopted data from MSNBC.COM website which is a free news data website with different categories of news and subjects. After generating the clusters by CURE algorithm, average of inter cluster and intra cluster are calculated and the results are compared with different similarity measures like Euclidean, Jaccard, projected Euclidean, cosine and fuzzy similarity. Finally behavior of clusters that made by CURE algorithm showed on a sequential data in a web usage domain with quantify our results by the way of explanations and list conclusions.
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تاریخ انتشار 2011