Unsupervised clustering on dynamic databases

Authors:
D. K. Tasoulis;M. N. Vrahatis
Affiliations:
Computational Intelligence Laboratory, Department of Mathematics, University of Patras, GR-26110 Patras, Greece and University of Patras Artificial Intelligence Research Center (UPAIRC), Universit ...;Computational Intelligence Laboratory, Department of Mathematics, University of Patras, GR-26110 Patras, Greece and University of Patras Artificial Intelligence Research Center (UPAIRC), Universit ...
Venue:
Pattern Recognition Letters
Year:
2005

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Abstract

Clustering algorithms typically assume that the available data constitute a random sample from a stationary distribution. As data accumulate over time the underlying process that generates them can change. Thus, the development of algorithms that can extract clustering rules in non-stationary environments is necessary. In this paper, we present an extension of the k-windows algorithm that can track the evolution of cluster models in dynamically changing databases, without a significant computational overhead. Experiments show that the k-windows algorithm can effectively and efficiently identify the changes on the pattern structure.