Frequency Sensitive Competitive Learning for Clustering on High-dimensional Hyperspheres
نویسندگان
چکیده
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.
منابع مشابه
Frequency Sensitive Competitive Learning for Balanced Clustering on High-dimensional Hyperspheres
Competitive learning mechanisms for clustering in general suffer from poor performance for very high dimensional ( ) data because of “curse of dimensionality” effects. In applications such as document clustering, it is customary to normalize the high dimensional input vectors to unit length, and it is sometimes also desirable to obtain balanced clusters, i.e., clusters of comparable sizes. The ...
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