A Novel Clustering Algorithm Based on Hierarchical and K-means Clustering

Abstract

Although the priority and randomicity to initiate clustering centers of K-means have been solved by traditional hierarchical k-means clustering algorithm, the algorithm is difficult to be applied widespread popularly owing to its high computational complexity. So a novel clustering algorithm based on hierarchical and K-means clustering, which has good computational complexity, is proposed in this paper. Firstly, the concept of silhouette coefficient is introduced and the optimal clustering number Kopt included in data set of unknown class information is decided. Then the distribution of data set is gotten through hierarchical clustering and clustering center is decided. Finally, the clustering is completed through K-means clustering. The efficiencies of the algorithm is validated through the test of IRIS testing data set.