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A clustering algorithm using an evolutionary programming-based approach
- Source :
- Pattern Recognition Letters. 18:975-986
- Publication Year :
- 1997
- Publisher :
- Elsevier BV, 1997.
-
Abstract
- In this paper, an evolutionary programming-based clustering algorithm is proposed. The algorithm effectively groups a given set of data into an optimum number of clusters. The proposed method is applicable for clustering tasks where clusters are crisp and spherical. This algorithm determines the number of clusters and the cluster centers in such a way that locally optimal solutions are avoided. The result of the algorithm does not depend critically on the choice of the initial cluster centers. ? 1997 Published by Elsevier Science B.V.
- Subjects :
- Optimization
Mathematical optimization
Fuzzy clustering
Clustering algorithms
Correlation clustering
Single-linkage clustering
Computer programming
Determining the number of clusters in a data set
ComputingMethodologies_PATTERNRECOGNITION
Artificial Intelligence
CURE data clustering algorithm
Pattern recognition
Nearest-neighbor chain algorithm
Signal Processing
Canopy clustering algorithm
Evolutionary programming
Computer Vision and Pattern Recognition
Cluster analysis
Algorithms
Software
Mathematics
Subjects
Details
- ISSN :
- 01678655
- Volume :
- 18
- Database :
- OpenAIRE
- Journal :
- Pattern Recognition Letters
- Accession number :
- edsair.doi.dedup.....5bf0b82c7f80e8661d26a9b520d048aa