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• W2085268214 abstract "Clustering validation indexes are intended to assess the goodness of clustering results. Many methods used to estimate the number of clusters rely on a validation index as a key element to find the correct answer. This paper presents a new validation index based on graph concepts, which has been designed to find arbitrary shaped clusters by exploiting the spatial layout of the patterns and their clustering label. This new clustering index is combined with a solid statistical detection framework, the gap statistic. The resulting method is able to find the right number of arbitrary-shaped clusters in diverse situations, as we show with examples where this information is available. A comparison with several relevant validation methods is carried out using artificial and gene expression data sets. The results are very encouraging, showing that the underlying structure in the data can be more accurately detected with the new clustering index. Our gene expression data results also indicate that this new index is stable under perturbation of the input data." @default.
• W2085268214 created "2016-06-24" @default.
• W2085268214 creator A5005860024 @default.
• W2085268214 creator A5007683173 @default.
• W2085268214 date "2013-03-01" @default.
• W2085268214 modified "2023-09-24" @default.
• W2085268214 title "How Many Clusters: A Validation Index for Arbitrary-Shaped Clusters" @default.
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• W2085268214 doi "https://doi.org/10.1109/tcbb.2013.32" @default.
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• W2085268214 hasPublicationYear "2013" @default.
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