Hybrid O(n √ n) clustering for sequential web usage mining

Authors:
Jianhua Yang;Ickjai Lee
Affiliations:
School of Computing and Maths, University of Western Sydney, Campbelltown, NSW, Australia;School of Information Technology, James Cook University, Townsville, QLD, Australia
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

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Abstract

We propose a natural neighbor inspired O($n \sqrt{n}$) hybrid clustering algorithm that combines medoid-based partitioning and agglomerative hierarchial clustering. This algorithm works efficiently by inheriting partitioning clustering strategy and operates effectively by following hierarchial clustering. More importantly, the algorithm is designed by taking into account the specific features of sequential data modeled in metric space. Experimental results demonstrate the virtue of our approach.