Adaptive clustering algorithms

Authors:
Alina Câmpan;Gabriela Şerban
Affiliations:
Department of Computer Science, “Babeş-Bolyai” University, Cluj-Napoca, Romania;Department of Computer Science, “Babeş-Bolyai” University, Cluj-Napoca, Romania
Venue:
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
Year:
2006

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Abstract

This paper proposes an adaptive clustering approach. We focus on re-clustering an object set, previously clustered, when the feature set characterizing the objects increases. We have developed adaptive extensions for two traditional clustering algorithms (k-means and Hierarchical Agglomerative Clustering). These extensions can be used for adjusting a clustering, that was established by applying the corresponding non-adaptive clustering algorithm before the feature set changed. We aim to reach the result more efficiently than applying the corresponding non-adaptive algorithm starting from the current clustering or from scratch. Experiments testing the method's efficiency are also reported.