A computational study of several relocation methods for k-means algorithms

摘要

The core of a k-means algorithm is the reallocation phase. A variety of schemes have been suggested for moving entities from one cluster to another and each of them may give a different clustering even though the data set is the same. The present paper describes shortcomings and relative merits of 17 relocation methods in connection with randomly generated data sets. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved. [References: 20]