Initializing KMeans Clustering Algorithm using Statistical Information
Mohammad F. Eltibi, Wesam M. Ashour · International Journal of Computer Applications · 2011
K-means clustering algorithm is one of the best known algorithms used in clustering; nevertheless it has many disadvantages as it may converge to a local optimum, depending on its random initialization of prototypes.We will propose an enhancement to the initialization process of k-means, which depends on using statistical information from the data set to initialize the prototypes.We show that our algorithm gives valid clusters, and that it decreases error and time.