Unsupervised clustering on dynamic databases

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.

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