High-dimensional clustering using frequency sensitive competitive learning

In this paper a clustering algorithm for sparsely sampled high-dimensional feature spaces is proposed. The algorithm performs clustering by employing a distance measure that compensates for differently sized clusters. A seque ntial version of the algorithm is constructed in the form of a frequency-se nsitive competitive learning scheme. Experiments are conducted on an artifi cial Gaussian data set and on wavelet-based texture feature sets, where cla ssification performance is used as a clustering significance measure. It is shown that the proposed technique improves classification performance dram atically for high-dimensional problems. (C) 1999 Pattern Recognition Societ y. Published by Elsevier Science Ltd. All rights reserved.