Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12358/24455
Title | Efficient and fast initialization algorithm for k-means clustering |
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Untitled | |
Abstract |
The famous K-means clustering algorithm is sensitive to the selection of the initial centroids and may converge to a local minimum of the criterion function value. A new algorithm for initialization of the K-means clustering algorithm is presented. The proposed initial starting centroids procedure allows the K-means algorithm to converge to a “better” local minimum. Our algorithm shows that refined initial starting centroids indeed lead to improved solutions. A framework for implementing and testing various clustering algorithms is presented and used for developing and evaluating the algorithm. |
Authors | |
Type | Journal Article |
Date | 2012 |
Published in | International Journal of Intelligent Systems and Applications |
Series | Volume: 4, Number: 1 |
Publisher | Modern Education and Computer Science Press |
Citation | |
Item link | Item Link |
License | ![]() |
Collections | |
Files in this item | ||
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Ashour, Wesam M._5.pdf | 929.9Kb |