Algorithms for clustering data
Algorithms for clustering data
Simulated annealing: theory and applications
Simulated annealing: theory and applications
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Boundary Detection by Constrained Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Mathematical Theory of Communication
A Mathematical Theory of Communication
A Deterministic Annealing Approach for Parsimonious Design of Piecewise Regression Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Natural discriminant analysis using interactive Potts models
Neural Computation
Computer Vision and Image Understanding - Special issue on Face recognition
Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
A unified framework for model-based clustering
The Journal of Machine Learning Research
Dual Clustering: Integrating Data Clustering over Optimization and Constraint Domains
IEEE Transactions on Knowledge and Data Engineering
General C-Means Clustering Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Novel vector quantiser design using reinforced learning as a pre-process
Signal Processing
Self-organizing maps and clustering methods for matrix data
Neural Networks - 2004 Special issue: New developments in self-organizing systems
A Competitive-Layer Model for Feature Binding and Sensory Segmentation
Neural Computation
A New Cluster Validity for Data Clustering
Neural Processing Letters
Constrained data clustering by depth control and progressive constraint relaxation
The VLDB Journal — The International Journal on Very Large Data Bases
A robust deterministic annealing algorithm for data clustering
Data & Knowledge Engineering
A Modified Deterministic Annealing Algorithm for Robust Image Segmentation
Journal of Mathematical Imaging and Vision
Novel Algorithm for Coexpression Detection in Time-Varying Microarray Data Sets
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Hierarchical, unsupervised learning with growing via phase transitions
Neural Computation
Statistical Mechanical Analysis of Fuzzy Clustering Based on Fuzzy Entropy
IEICE - Transactions on Information and Systems
Accurate estimation of ICA weight matrix by implicit constraint imposition using lie group
IEEE Transactions on Neural Networks
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
An entropy-based framework for dynamic clustering and coverage problems
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Robust learning of mixture models and its application on trial pruning for EEG signal analysis
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
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A deterministic annealing approach to clustering is derived on the basis of the principle of maximum entropy. This approach is independent of the initial state and produces natural hierarchical clustering solutions by going through a sequence of phase transitions. It is modified for a larger class of optimization problems by adding constraints to the free energy. The concept of constrained clustering is explained, and three examples are are given in which it is used to introduce deterministic annealing. The previous clustering method is improved by adding cluster mass variables and a total mass constraint. The traveling salesman problem is reformulated as constrained clustering, yielding the elastic net (EN) approach to the problem. More insight is gained by identifying a second Lagrange multiplier that is related to the tour length and can also be used to control the annealing process. The open path constraint formulation is shown to relate to dimensionality reduction by self-organization in unsupervised learning. A similar annealing procedure is applicable in this case as well.