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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Câmpan, A., & Şerban, G. (2006). Adaptive clustering algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4013 LNAI, pp. 407–418). Springer Verlag. https://doi.org/10.1007/11766247_35
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