Title :
Unsupervised pattern clustering for data mining
Author :
Wilamowska, Katarzyna ; Manic, Milos
Author_Institution :
Dept. of Comput. Sci., Wyoming Univ., Laramie, WY, USA
Abstract :
The problem of clustering multidimensional data with similar properties has been targeted in literature. In this paper, the authors have concentrated on the drawback of one of the often used methods, mountain clustering. A method that overcomes this problem is proposed. The method is tested on examples and results are graphically depicted
Keywords :
data mining; pattern clustering; unsupervised learning; Kohonen winner-take-all learning; data mining; mountain clustering; multidimensional data clustering; unsupervised pattern clustering; Clustering algorithms; Clustering methods; Computer science; Data analysis; Data mining; Functional analysis; Multidimensional systems; Neurons; Pattern clustering; Testing;
Conference_Titel :
Industrial Electronics Society, 2001. IECON '01. The 27th Annual Conference of the IEEE
Conference_Location :
Denver, CO
Print_ISBN :
0-7803-7108-9
DOI :
10.1109/IECON.2001.975574
