Cure: Clustering on Sequential Data for Web Personalization: Tests and Experimental Results
The world wide web is full of multi-disciplinary data for knowledge data discovery research. In this paper, the authors present CURE (Clustering Usage REpresentatives) algorithm to find clusters on a web usage data. They 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 the results by the way of explanations and list conclusions.