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Efficient mining of traversal patterns
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IEEE Internet Computing
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ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
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ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
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ACM SIGKDD Explorations Newsletter
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ACM Transactions on Internet Technology (TOIT)
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ACSC '08 Proceedings of the thirty-first Australasian conference on Computer science - Volume 74
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A web-page usage prediction scheme using weighted suffix trees
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International Journal of Knowledge and Web Intelligence
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PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Existing web usage mining techniques focus only on discovering knowledge based on the statistical measures obtained from the static characteristics of web usage data. They do not consider the dynamic nature of web usage data. In this paper, we focus on discovering novel knowledge by analyzing the change patterns of historical web access sequence data. We present an algorithm called WAM-MINER to discover Web Access Motifs (WAMs). WAMs are web access patterns that never change or do not change significantly most of the time (if not always) in terms of their support values during a specific time period. WAMs are useful for many applications, such as intelligent web advertisement, web site restructuring, business intelligence, and intelligent web caching.