Frequency sensitive competitive learning for clustering on high-dimensional hyperspheres

This paper derives three competitive learning mechanisms from first principles to obtain clusters of comparable sizes when both inputs and representatives are normalized. These mechanisms are very effective in achieving balanced grouping of inputs in high dimensional spaces as illustrated by experimental results on clustering two popular text data sets in 26,099 and 21,839 dimensional spaces, respectively.