Multiseed clustering algorithm based on max-min distance means
基于最大最小距离法的多中心聚类算法

A novel multiseed clustering algorithm was proposed aiming at shortcomings of k-means algorithm. This algorithm could find optimal initial starting points applying iterative max-rain distance means and then combined the small clusters from given data set into final ones, for an elongated or large cluster could be considered as the union of a few small distinct hyperspherieal clusters. Experimcntal results demonstrate that the improved algorithm can automatically obtain the number of initial clusters, be effective on data set of irregular shapes and lead to better solutions than k-means algorithm.