DocumentCode
2657899
Title
Deterministic annealing, constrained clustering, and optimization
Author
Rose, K. ; Gurewitz, E. ; Fox, G.C.
Author_Institution
Caltech Concurrent Comput. Program, California Inst. of Technol., Pasadena, CA, USA
fYear
1991
fDate
18-21 Nov 1991
Firstpage
2515
Abstract
In previous work the authors (Phys. Rev. Let., vol.65, p.945-8, 1990) proposed the concept of deterministic annealing for the problem of clustering and vector quantization. This approach is summarized. The authors extend the clustering method to the constraint clustering method. Adding constraints to the deterministic annealing mechanism expands the variety of optimization problems which can be solved by this method. A brief presentation of the clustering approach is given. Two examples to which the constraint clustering approach can be applied are included
Keywords
neural nets; pattern recognition; simulated annealing; constrained clustering; deterministic annealing; optimization; vector quantization; Clustering algorithms; Clustering methods; Concurrent computing; Constraint optimization; Cost function; Entropy; Probability distribution; Simulated annealing; Stochastic processes; Vector quantization;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN
0-7803-0227-3
Type
conf
DOI
10.1109/IJCNN.1991.170767
Filename
170767
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