An objective evaluation criterion for clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
2005 Special Issue: Efficient streaming text clustering
Neural Networks - 2005 Special issue: IJCNN 2005
Spatial fading correlation model using mixtures of Von Mises Fisher distributions
IEEE Transactions on Wireless Communications
Competitive repetition-suppression (core) learning
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Bag of spatio-visual words for context inference in scene classification
Pattern Recognition
On online high-dimensional spherical data clustering and feature selection
Engineering Applications of Artificial Intelligence
Evolving soft subspace clustering
Applied Soft Computing
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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 are applicable to static data and produced high-quality and well-balanced clusters for high-dimensional data. Like kmeans, each iteration is linear in the number of data points and in the number of clusters for all the three algorithms. A frequency-sensitive algorithm to cluster streaming data is also proposed. Experimental results on clustering of high-dimensional text data sets are provided to show the effectiveness and applicability of the proposed techniques.