Online Clustering Algorithms for Radar Emitter Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effective clustering and boundary detection algorithm based on Delaunay triangulation
Pattern Recognition Letters
Data mining to cluster human performance by using online self regulating clustering method
MAASE'08 Proceedings of the 1st WSEAS International Conference on Multivariate Analysis and its Application in Science and Engineering
Generalized fuzzy C-means clustering algorithm with improved fuzzy partitions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Clustering: A neural network approach
Neural Networks
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
An N-parallel multivalued network: applications to the travelling salesman problem
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Multiple self-splitting and merging competitive learning algorithm
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Energy based competitive learning
Neurocomputing
A novel clustering method for analysis of gene microarray expression data
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
Improved self-splitting competitive learning algorithm
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
OPTOC-based clustering analysis of gene expression profiles in spectral space
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
k'-Means algorithms for clustering analysis with frequency sensitive discrepancy metrics
Pattern Recognition Letters
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Clustering in the neural-network literature is generally based on the competitive learning paradigm. The paper addresses two major issues associated with conventional competitive learning, namely, sensitivity to initialization and difficulty in determining the number of prototypes. In general, selecting the appropriate number of prototypes is a difficult task, as we do not usually know the number of clusters in the input data a priori. It is therefore desirable to develop an algorithm that has no dependency on the initial prototype locations and is able to adaptively generate prototypes to fit the input data patterns. We present a new, more powerful competitive learning algorithm, self-splitting competitive learning (SSCL), that is able to find the natural number of clusters based on the one-prototype-take-one-cluster (OPTOC) paradigm and a self-splitting validity measure. It starts with a single prototype randomly initialized in the feature space and splits adaptively during the learning process until all clusters are found; each cluster is associated with a prototype at its center. We have conducted extensive experiments to demonstrate the effectiveness of the SSCL algorithm. The results show that SSCL has the desired ability for a variety of applications, including unsupervised classification, curve detection, and image segmentation