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. Index Terms-data mining , K-means initialization m pattern recognition
The performance of the standard k-means algorithm will be affected by the selection of the initial centers and converges to local minimum problems REF .
Efficient and Fast Initialization Algorithm for K-means Clustering
International Journal of Intelligent Systems and Applications
Computer Science