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2010
Springer

Estimation of the Number of Clusters Using Multiple Clustering Validity Indices

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Estimation of the Number of Clusters Using Multiple Clustering Validity Indices
One of the challenges in unsupervised machine learning is finding the number of clusters in a dataset. Clustering Validity Indices (CVI) are popular tools used to address this problem. A large number of CVIs have been proposed, and reports that compare different CVIs suggest that no single CVI can always outperform others. Following suggestions found in prior art, in this paper we formalize the concept of using multiple CVIs for cluster number estimation in the framework of multi-classifier fusion. Using a large number of datasets, we show that decision-level fusion of multiple CVIs can lead to significant gains in accuracy in estimating the number of clusters, in particular for highdimensional datasets with large number of clusters. Key words: clustering, clustering validity indices, multiple classifier
Krzysztof Kryszczuk, Paul Hurley
Added 18 May 2010
Updated 18 May 2010
Type Conference
Year 2010
Where MCS
Authors Krzysztof Kryszczuk, Paul Hurley
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