Classification algorithms
Algorithms for clustering data
Algorithms for clustering data
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A deterministic annealing approach to clustering
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Robust Line Fitting in a Noisy Image by the Method of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition
Efficient Training of RBF Networks Via the BYY Automated Model Selection Learning Algorithms
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Dealing with non-metric dissimilarities in fuzzy central clustering algorithms
International Journal of Approximate Reasoning
An Experimental Comparison of Kernel Clustering Methods
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Clustering: A neural network approach
Neural Networks
Enhanced neural gas network for prototype-based clustering
Pattern Recognition
Skeletonization of noisy images via the method of legendre moments
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Local pattern detection and clustering
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
The fuzzy mega-cluster: robustifying FCM by scaling down memberships
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
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A new operational definition of cluster is proposed, and a fuzzy clustering algorithm with minimal biases is formulated by making use of the maximum entropy principle to maximize the entropy of the centroids with respect to the data points (clustering entropy). The authors make no assumptions on the number of clusters or their initial positions. For each value of an adimensional scale parameter /spl beta/', the clustering algorithm makes each data point iterate towards one of the cluster's centroids, so that both hard and fuzzy partitions are obtained. Since the clustering algorithm can make a multiscale analysis of the given data set one can obtain both hierarchy and partitioning type clustering. The relative stability with respect to /spl beta/' of each cluster structure is defined as the measurement of cluster validity. The authors determine the specific value of /spl beta/' which corresponds to the optimal positions of cluster centroids by minimizing the entropy of the data points with respect to the centroids (clustered entropy). Examples are given to show how this least biased method succeeds in getting perceptually correct clustering results.