An algorithm for multidimensional data clustering

Abstract

A new divisive algorithm for multidimensional data clustering is suggested. Based on the minimization of the sum-of-squared-errors, the proposed method produces much smaller quantization errors than the median-cut and mean-split algorithms. It is also observed that the solutions obtained from our algorithm are close to the local optimal ones derived by the k-means iterative procedure.