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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2023.3237667
|2 doi
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|a eng
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| 100 |
1 |
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|a Liu, Hongfu
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Transforming Complex Problems Into K-Means Solutions
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|c 2023
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 06.06.2023
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|a Date Revised 21.09.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a K-means is a fundamental clustering algorithm widely used in both academic and industrial applications. Its popularity can be attributed to its simplicity and efficiency. Studies show the equivalence of K-means to principal component analysis, non-negative matrix factorization, and spectral clustering. However, these studies focus on standard K-means with squared euclidean distance. In this review paper, we unify the available approaches in generalizing K-means to solve challenging and complex problems. We show that these generalizations can be seen from four aspects: data representation, distance measure, label assignment, and centroid updating. As concrete applications of transforming problems into modified K-means formulation, we review the following applications: iterative subspace projection and clustering, consensus clustering, constrained clustering, domain adaptation, and outlier detection
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|a Journal Article
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| 700 |
1 |
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|a Chen, Junxiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Dy, Jennifer
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Fu, Yun
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 7 vom: 20. Juli, Seite 9149-9168
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
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|g volume:45
|g year:2023
|g number:7
|g day:20
|g month:07
|g pages:9149-9168
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|u http://dx.doi.org/10.1109/TPAMI.2023.3237667
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