Frequency-sensitive competitive learning for scalable balanced clustering on high-dimensional hyperspheres

IEEE Transactions on Neural Networks
Arindam Banerjee, Joydeep Ghosh

Abstract

Competitive learning mechanisms for clustering, in general, suffer from poor performance for very high-dimensional (>1000) 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 spherical kmeans (spkmeans) algorithm, which normalizes the cluster centers as well as the inputs, has been successfully used to cluster normalized text documents in 2000+ dimensional space. Unfortunately, like regular kmeans and its soft expectation-maximization-based version, spkmeans tends to generate extremely imbalanced clusters in high-dimensional spaces when the desired number of clusters is large (tens or more). This paper first shows that the spkmeans algorithm can be derived from a certain maximum likelihood formulation using a mixture of von Mises-Fisher distributions as the generative model, and in fact, it can be considered as a batch-mode version of (normalized) competitive learning. The proposed generative model is then adapted in a principled way to yield three frequency-sensitive competitive learning variants that a...Continue Reading

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Citations

Aug 9, 2005·Neural Networks : the Official Journal of the International Neural Network Society·Shi Zhong
Dec 22, 2014·Neural Networks : the Official Journal of the International Neural Network Society·Jian-Sheng WuJian-Huang Lai
Oct 14, 2016·SpringerPlus·Xiaoyan Wang, Yanping Bai
Sep 23, 2016·Computational Intelligence and Neuroscience·Xiaoyan Wang, Yanping Bai

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