Bhushan Shankar Suryavanshi

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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(More)
A number of approaches which use model-based collaborative filtering (CF) for scalability in building recommendation systems in web personalization have poor accuracy due to the fact that web usage data is often sparse and noisy. Clustering, mining association rules, and sequence pattern discovely have been used to determine the access behavior model.(More)
A primary application of web usage profiling is in model-based collaborative filtering (CF) for building recommender systems used for web personalization. CF techniques used for recommendation require accumulation of vast amount of historical user-preference information, which is queried to provide a personalized experience. Model-based CF techniques are(More)
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