Efficient K-medoids clustering algorithm
一种高效的K-medoids聚类算法

Due to the disadvantages of sensitivity to the initial selection of the medoids and poor performance in large data set processing in the K-medoids clustering algorithm, this paper proposed an improved K-medoids algorithm based on a fine-tuned of initial medoids and an incremental candidate set of medoids. The proposed algorithm optimized initial medoids by fine-tu-ning and reduced computational complexity of medoids substitution through expanding medoids candidate set gradually. Expenrimental results demonstrate the effectiveness of this algorithm,which can improve clustering quality and significantly shorten the time in calculation compared with the traditional K-medoids algorithm.