15th International Conference on Pattern Recognition (ICPR'00) - Volume 3 Fully Unsupervised Fuzzy Clustering with Entropy Criterion Barcelona, Spain September 03-September 08 ISBN: 0-7695-0750-6
Herein we present a fully unsupervised clustering algorithm in order to overcome the problem of a priori defining the number of clusters. We propose to optimize an objective function, which is the sum of two terms. The first one is a generalization of intra-cluster distance within the framework of fuzzy sets. The second one is an entropy term. Our clustering algorithm has been applied to the problem of clustering both remote sensed data and medical images.
Citation:
Anne Lorette, Xavier Descombes, Josiane Zerubia, "Fully Unsupervised Fuzzy Clustering with Entropy Criterion," icpr, vol. 3, pp.3998, 15th International Conference on Pattern Recognition (ICPR'00) - Volume 3, 2000 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||