K-means properties on six clustering benchmark datasets |
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Authors: | Pasi Fränti Sami Sieranoja |
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Affiliation: | 1.Machine Learning Group, School of Computing,University of Eastern Finland,Joensuu,Finland |
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Abstract: | This paper has two contributions. First, we introduce a clustering basic benchmark. Second, we study the performance of k-means using this benchmark. Specifically, we measure how the performance depends on four factors: (1) overlap of clusters, (2) number of clusters, (3) dimensionality, and (4) unbalance of cluster sizes. The results show that overlap is critical, and that k-means starts to work effectively when the overlap reaches 4% level. |
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