A deterministic annealing approach to clustering
Pattern Recognition Letters
Gaussian clustering method based on maximum-fuzzy-entropy interpretation
Fuzzy Sets and Systems
Entropy-based subspace clustering for mining numerical data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Entropy-based fuzzy clustering and fuzzy modeling
Fuzzy Sets and Systems
Information Theoretic Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
COOLCAT: an entropy-based algorithm for categorical clustering
Proceedings of the eleventh international conference on Information and knowledge management
Extreme physical information and objective function in fuzzy clustering
Fuzzy Sets and Systems - Clustering and modeling
Three-way fuzzy clustering models for LR fuzzy time trajectories
Computational Statistics & Data Analysis
A new clustering evaluation function using Renyi's information potential
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
IEEE Transactions on Fuzzy Systems
Fuzzy Clustering for Data Time Arrays With Inlier and Outlier Time Trajectories
IEEE Transactions on Fuzzy Systems
The fuzzy approach to statistical analysis
Computational Statistics & Data Analysis
Data analysis with fuzzy clustering methods
Computational Statistics & Data Analysis
Fuzzy clustering of time series in the frequency domain
Information Sciences: an International Journal
Wavelets-based clustering of multivariate time series
Fuzzy Sets and Systems
Objective function-based clustering
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Relative entropy fuzzy c-means clustering
Information Sciences: an International Journal
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Fuzzy unsupervised clustering models based on entropy regularization are suggested in order to classify time-varying data. In particular, in the proposed models, objective functions, which are the sum of two terms, are minimized. The first term is a dynamic generalization of intra-cluster distance, in a fuzzy framework, that takes into account the instantaneous and/or longitudinal features of the time-varying observations (the so-called multivariate time trajectories); in this way, the within cluster dispersion is minimized (maximize the internal cohesion). The second term represents the Shannon entropy measure as applied to fuzzy partitions (entropy regularization); then, a given measure of entropy is maximized or, equivalently, the converse of the entropy is minimized. Overall, the total functional depending on both the previous aspects is optimized. The dynamic fuzzy entropy clustering models have been applied to a meteorological dataset and an empirical comparison with the instantaneous and/or longitudinal fuzzy C-means clustering models has been made.