DocumentCode
3001780
Title
Unsupervised hierarchical clustering via a genetic algorithm
Author
Greene, William A.
Author_Institution
Dept. of Comput. Sci., New Orleans Univ., USA
Volume
2
fYear
2003
fDate
8-12 Dec. 2003
Firstpage
998
Abstract
We present a clustering algorithm which is unsupervised, incremental, and hierarchical. The algorithm is distance-based and creates centroids. Then we combine the power of evolutionary forces with the clustering algorithm, counting on good clusterings to evolve to yet better ones. We apply our approach to standard data sets, and get very good results. Finally, we use bagging to pool the results of different clustering trials, and again get very good results.
Keywords
genetic algorithms; pattern clustering; unsupervised learning; centroids; evolutionary techniques; genetic algorithm; incremental learning; unsupervised hierarchical clustering algorithm; Clustering algorithms; Clustering methods; Computer science; Counting circuits; Data analysis; Genetic algorithms; Genetic mutations; Partitioning algorithms; Speech analysis; Unsupervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2003. CEC '03. The 2003 Congress on
Print_ISBN
0-7803-7804-0
Type
conf
DOI
10.1109/CEC.2003.1299776
Filename
1299776
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