K-means properties on six clustering benchmark datasets
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) unb...
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| Published in | Applied intelligence (Dordrecht, Netherlands) Vol. 48; no. 12; pp. 4743 - 4759 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.12.2018
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
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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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| AbstractList | 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. |
| Author | Fränti, Pasi Sieranoja, Sami |
| Author_xml | – sequence: 1 givenname: Pasi orcidid: 0000-0002-9554-2827 surname: Fränti fullname: Fränti, Pasi email: pasi.franti@uef.fi organization: Machine Learning Group, School of Computing, University of Eastern Finland – sequence: 2 givenname: Sami surname: Sieranoja fullname: Sieranoja, Sami organization: Machine Learning Group, School of Computing, University of Eastern Finland |
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| Keywords | k-means Benchmark Clustering quality Clustering algorithms |
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| SubjectTerms | Artificial Intelligence Benchmarks Clustering Computer Science Machines Manufacturing Mechanical Engineering Processes Unbalance |
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| Title | K-means properties on six clustering benchmark datasets |
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