Mining significant usage patterns from clickstream data

Authors:
Lin Lu;Margaret Dunham;Yu Meng
Affiliations:
Department of Computer Science and Engineering, Southern Methodist University, Dallas, Texas;Department of Computer Science and Engineering, Southern Methodist University, Dallas, Texas;Department of Computer Science and Engineering, Southern Methodist University, Dallas, Texas
Venue:
WebKDD'05 Proceedings of the 7th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
Year:
2005

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Abstract

Discovery of usage patterns from Web data is one of the primary purposes for Web Usage Mining. In this paper, a technique to generate Significant Usage Patterns (SUP) is proposed and used to acquire significant “user preferred navigational trails”. The technique uses pipelined processing phases including sub-abstraction of sessionized Web clickstreams, clustering of the abstracted Web sessions, concept-based abstraction of the clustered sessions, and SUP generation. Using this technique, valuable customer behavior information can be extracted by Web site practitioners. Experiments conducted using Web log data provided by J.C.Penney demonstrate that SUPs of different types of customers are distinguishable and interpretable. This technique is particularly suited for analysis of dynamic websites.