A K-means optimization algorithm based on relative core cluster

Abstract

With the rapid development of the technology of cluster analysis, people have proposed a lot of clustering algorithms, such as the K-means clustering algorithm which is simple, low complexity and has been used widely, and it has been the improved object or base for many other algorithms. This paper presents a K-means optimization algorithm based on relative core cluster -RCBK-means. The algorithm is based on the core group, uses the center of the relative core cluster of the data set as the initial center of the K-means algorithm, thus avoiding the local optimization problem of the clustering results which caused by selecting the initial center randomly of the classic K-means algorithm, and improving the algorithm results effectively. © 2012 Springer-Verlag GmbH.