Efficient and Fast Initialization Algorithm for K-means Clustering
Автор: Mohammed El Agha, Wesam M. Ashour
Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa
Статья в выпуске: 1 vol.4, 2012 года.
Бесплатный доступ
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.
Data mining, K-means initialization m pattern recognition
Короткий адрес: https://sciup.org/15010089
IDR: 15010089
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