An efficient k-means clustering algorithms: Analysis and implementation
In k-means clustering, we are given a set of n data points in d-dimensional space R super(d) and an integer k and the problem is to determine a set of k points in R super(d), called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic f...
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence Jg. 24; H. 7; S. 881 - 892 |
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| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
01.07.2002
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| ISSN: | 0162-8828 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | In k-means clustering, we are given a set of n data points in d-dimensional space R super(d) and an integer k and the problem is to determine a set of k points in R super(d), called centers, so as to minimize the mean squared distance from each data point to its nearest center. A popular heuristic for k-means clustering is Lloyd's algorithm. In this paper, we present a simple and efficient implementation of Lloyd's k-means clustering algorithm, which we call the filtering algorithm. This algorithm is easy to implement, requiring a kd-tree as the only major data structure. We establish the practical efficiency of the filtering algorithm in two ways. First, we present a data-sensitive analysis of the algorithm's running time, which allows that the algorithm runs faster as the separation between clusters increases. Second, we present a number of empirical studies both on synthetically generated data and on real data sets from applications in color quantization, data compression, and image segmentation. |
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| Bibliographie: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
| ISSN: | 0162-8828 |
| DOI: | 10.1109/TPAMI.2002.1017616 |