An evolutionary clustering algorithm

Abstract

There are many heuristic algorithms for clustering, from which the most important are the hierarchical methods of agglomeration, especially the Ward's method. Among the iterative methods the most universally used is the C-means method and it's generalizations. These methods have many advantages, but they are more or less dependent on the distribution of points in space and the shape of clusters. In this paper the problem of clustering is treated as a problem of optimization of a certain quality index. For that problem the author proposes two solutions: a hierarchical partitioning algorithm and an evolutionary algorithm.