Title :
Unsupervised hierarchical clustering via a genetic algorithm
Author :
Greene, William A.
Author_Institution :
Dept. of Comput. Sci., New Orleans Univ., USA
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;
Conference_Titel :
Evolutionary Computation, 2003. CEC '03. The 2003 Congress on
Print_ISBN :
0-7803-7804-0
DOI :
10.1109/CEC.2003.1299776
