Exploring the Pattern of Customer Purchase with Web Usage Mining
The purpose of this paper is to do an analysis of the sample / raw data to obtain a meaningful interpretation using some of the data mining algorithms like a vector quantization based clustering and then an ‘Apriori’ based Association rule mining algorithm. Web session clustering plays a key role to classify web visitors on the basis of user click history and similarity measure. An important application of chronological mining techniques is web usage mining, for mining web log accesses, where the sequences of web page accesses made by different web users over a period of time, through a server, are recorded. The experiment will be conducted base on the idea of Apriori algorithm along with VQ based clustering, which first stores the original web access sequence database for storing non-sequential data. The experimental result will be given with analysis on further refinement. This is aimed at a meaningful segregation of the various customers based on their RFM values, as well to find out relationships and patterns among the purchases made by the customer, over several transactions.
KeywordsApriori VQ Chronological mining Web Usage Data Mining RFM
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