To Enhance Web Response Time Using Agglomerative Clustering Technique for Web Navigation Recommendation

Abstract

An organization needs to comprehend their customers and clients conduct, inclinations, and future needs which rely on their past conduct. Web usage mining is an intuitive research point in which customers and clients’ session grouping is done to comprehend the exercises. This exploration examines the issue of mining and breaking down incessant example and particularly centered around lessening the quantity of standards utilizing shut example procedure and it likewise diminish filters the span of the database utilizing agglomerative grouping strategy. In the present work, a novel technique for design mining is introduced to tackle the issue through profile-based closed sequential pattern mining utilizing agglomerative clustering (PCSPAC). In this research, the proposed method is an improved version of Weblog mining techniques and to the online navigational pattern forecasting. In the proposed approach, first, we store the Web data which is accessed by the user and then find the pattern. Items with the same pattern are merged and then the closed frequent set of Web pages is found. Main advantage of our approach is that when the user next time demands for the same item then it will search only partial database, not in whole data. There is no need to take input as number of clusters. Experimental results illustrate that proposed approach reduces the search time with more accuracy.