Similarity-Based Fuzzy Clustering for User Profiling
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
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WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
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KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
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ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
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We propose an efficient technique for mining web usage profiles based on subtractive clustering that scales to large datasets. Unlike earlier clustering based techniques for the same purpose, our technique does not require user specification of any input parameter to obtain the desired clustering. Instead, we achieve this by searching in the cluster space for the best clustering of the given web usage data. To evaluate clustering quality, we have formulated a validity index for our algorithm. Our implementation of the proposed technique and the experiments with large real life datasets show that it indeed mines the desired usage profiles much faster than existing techniques.