Hybrid agglomerative clustering for large databases: an efficient interactivity approach

Authors:
Ickjai Lee;Jianhua Yang
Affiliations:
School of Information Technology, James Cook University, Townsville, QLD, Australia;School of Computing and Information Technology, University of Western Sydney, Campbelltown, NSW, Australia
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

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Abstract

This paper presents a novel hybrid clustering approach that takes advantage of the efficiency of k-Means clustering and the effectiveness of hierarchical clustering. It employs the combination of geometrical information defined by k-Means and topological information formed by the Voronoi diagram to advantage. Our proposed approach is able to identify clusters of arbitrary shapes and clusters of different densities in O(n) time. Experimental results confirm the effectiveness and efficiency of our approach.