%0 Journal Article
%T Efficient and Fast Initialization Algorithm for K-means Clustering
%A Mohammed El Agha
%A Wesam M. Ashour
%J International Journal of Intelligent Systems and Applications
%D 2012
%I MECS Publisher
%X 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 ˇ°betterˇ± 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.
%K data mining
%K K-means initialization m pattern recognition
%U http://www.mecs-press.org/ijisa/ijisa-v4-n1/v4n1-3.html