Title :
A hierarchical approach to ART-like clustering algorithm
Author :
Su, Mu-Chun ; Liu, Yi-Chun
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Central Univ., Chung-li, Taiwan
fDate :
6/24/1905 12:00:00 AM
Abstract :
We propose a hierarchical approach to ART-like clustering algorithm which is able to deal with data consisting of arbitrarily geometrical-shaped clusters. A combined hierarchical and ART-like clustering is suggested as a natural feasible solution to the two problems of determining the number of clusters and clustering data. A 2D artificial data set is tested to demonstrate the performance of the proposed algorithm
Keywords :
ART neural nets; learning (artificial intelligence); pattern clustering; ART networks; adaptive resonance theory; clustering algorithms; data set; hierarchical clustering; learning; neural network; Clustering algorithms; Computer science; Data engineering; Neural networks; Partitioning algorithms; Resonance; Shape measurement; Subspace constraints; Testing; Unsupervised learning;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1005574
