DocumentCode :
1743029
Title :
Clustering combination method
Author :
Qian, Yuntao ; Suen, Ching Y.
Volume :
2
fYear :
2000
fDate :
2000
Firstpage :
732
Abstract :
Clustering combination uses more than one clustering method with identical pattern features to improve the clustering performance. In general clustering is an optimization procedure based on a specific clustering criterion, so clustering combination can be regarded as a technique that constructs and processes multiple clustering criteria rather than a single criterion. We propose two methods of combining objective function clustering and graph theory clustering. One incorporates multiple criteria into an objective function according to their importance, and solves this problem with constrained nonlinear optimization programming. The other method consists of two sequential procedures: (a) a traditional objective function clustering for generating the initial result, and (b) an autoassociative additive system based on graph theory clustering for modifying the initial result
Keywords :
graph theory; nonlinear programming; optimisation; pattern clustering; autoassociative additive system; clustering combination; constrained nonlinear optimization programming; graph theory clustering; identical pattern features; multiple clustering criteria; objective function clustering; optimization procedure; Clustering algorithms; Clustering methods; Computer science; Constraint optimization; Data analysis; Distance measurement; Functional programming; Graph theory; Neural networks; Statistics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2000. Proceedings. 15th International Conference on
Conference_Location :
Barcelona
ISSN :
1051-4651
Print_ISBN :
0-7695-0750-6
Type :
conf
DOI :
10.1109/ICPR.2000.906179
Filename :
906179
Link To Document :
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