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variables (e.g., customer loyalty) and on using cluster analysis and mixture models for segmentation. The data mining literature has provided various clustering algorithms for segmentation without focusing specifically on clustering customer transactions. Building on the notion that observable customer transactions are generated by latent behavioral traits, in this paper, we investigate using a pattern-based clustering approach to grouping customer transactions. We define an objective function that we maximize in order to achieve a good clustering of customer transactions and present an algorithm,GHIC, that groups customer transactions such that item sets generated from each cluster, while similar to each other, are different from ones generated from others. We present experimental results from user-centric Web usage data that demonstrates that GHIC generates a highly effective clustering of transactions
Technology Used : Java , Swing
Algorithm : Clustering

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