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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Cabria, I. Gondra, I. |
| Copyright Year | 2012 |
| Abstract | Because of its conceptual simplicity, k-means is one of the most commonly used clustering algorithms. However, its performance in terms of global optimality depends heavily on both the selection of k and the selection of the initial cluster centers. On the other hand, Mean Shift clustering does not rely upon a priori knowledge of the number of clusters. Furthermore, it finds the modes of the underlying probability density function of the observations, which would be a good choice of initial cluster centers for k-means. We present a Mean Shift-based initialization method for k-means. A comparative study of the proposed and other initialization methods is performed on two real-life problems with very large amounts of data: Facility Location and Molecular Dynamics. In the study, the proposed initialization method outperforms the other methods in terms of clustering performance. |
| Starting Page | 579 |
| Ending Page | 586 |
| File Size | 595459 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781467348737 |
| DOI | 10.1109/CIT.2012.124 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-10-27 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | mean shift Heuristic algorithms Europe Electronic waste Partitioning algorithms molecular dynamics Convergence facility location Clustering algorithms k-means initialization clustering Recycling |
| Content Type | Text |
| Resource Type | Article |
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