Competitive learning algorithms for vector quantization
Neural Networks
Probabilistic validation approach for clustering
Pattern Recognition Letters
Bayesian Ying-Yang machine, clustering and number of clusters
Pattern Recognition Letters - special issue on pattern recognition in practice V
CUBIC: Identification of Regulatory Binding Sites Through Data Clustering
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Cluster number selection for a small set of samples using the Bayesian Ying-Yang model
IEEE Transactions on Neural Networks
An efficient k'-means clustering algorithm
Pattern Recognition Letters
A customized Gabor filter for unsupervised color image segmentation
Image and Vision Computing
Field independent probabilistic model for clustering multi-field documents
Information Processing and Management: an International Journal
Energy based competitive learning
Neurocomputing
Unsupervised Feature Selection with Feature Clustering
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Magnitude Sensitive Competitive Learning
Neurocomputing
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The existing Rival Penalized Competitive Learning (RPCL) algorithm and its variants have provided an attractive way to perform data clustering without knowing the exact number of clusters. However, their performance is sensitive to the preselection of the rival delearning rate. In this paper, we further investigate the RPCL and present a mechanism to control the strength of rival penalization dynamically. Consequently, we propose the Rival Penalization Controlled Competitive Learning (RPCCL) algorithm and its stochastic version. In each of these algorithms, the selection of the delearning rate is circumvented using a novel technique. We compare the performance of RPCCL to RPCL in Gaussian mixture clustering and color image segmentation, respectively. The experiments have produced the promising results.