Title :
Dynamic partitional clustering using evolution strategies
Author :
Lee, C.-Y. ; Antonsson, E.K.
Author_Institution :
Dept. of Mech. Eng., California Inst. of Technol., Pasadena, CA, USA
Abstract :
A novel evolution strategy implementing variable length genomes is developed to address the problem of dynamic partitional clustering. As opposed to static, dynamic partitional clustering does not require the a priori specification of the number of clusters. Results of the algorithm are presented and discussed for 2-D touching and non-touching cluster test cases
Keywords :
computational complexity; data analysis; data structures; 2-D touching; a priori specification; data analysis; data structures; dynamic partitional clustering; evolution strategies; variable length genomes; Automation; Bioinformatics; Clustering algorithms; Data analysis; Dynamic programming; Genomics; Humans; Mechanical engineering; Partitioning algorithms; Testing;
Conference_Titel :
Industrial Electronics Society, 2000. IECON 2000. 26th Annual Confjerence of the IEEE
Conference_Location :
Nagoya
Print_ISBN :
0-7803-6456-2
DOI :
10.1109/IECON.2000.972427
