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.