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.
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Câmpan, A., Şerban, G. (2006). Adaptive Clustering Algorithms. In: Lamontagne, L., Marchand, M. (eds) Advances in Artificial Intelligence. Canadian AI 2006. Lecture Notes in Computer Science(), vol 4013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11766247_35
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DOI: https://doi.org/10.1007/11766247_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34628-9
Online ISBN: 978-3-540-34630-2
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