Towards enhancement of performance of K-means clustering using nature-inspired optimization algorithms.
Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms h...
| Published in: | Scientific World Journal pp. 564829 - 564830 |
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| Main Authors: | , , , |
| Format: | research Journal Article |
| Published: |
Wiley-Blackwell
2014
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| Online Access: | View this record in EBSCOhost |