Multidimensional data clustering utilizing hybrid search strategies

Abstract

This paper presents two hybrid search strategies for the efficient solution of the data clustering problem based on the minimum variance approach. The proposed algorithms basically alternate between a depth-first search and a breadth-first search to effectively minimize the underlying objective function. Extensive experimentation shows that the proposed strategies are consistently superior to the popular K-MEANS algorithm as well as to other techniques based on a single search strategy.