An improved method for fuzzy clustering

Author

Ilhan, Sevinc ; Duru, Nevcihan

Author_Institution

Sch. of Comput. Eng., Kocaeli Univ., Kocaeli, Turkey

fYear

2009

fDate

2-4 Sept. 2009

Firstpage

1

Lastpage

4

Abstract

Adaptive resonance theory (ART) is an unsupervised neural network. Fuzzy ART is a variation of ART, allows both binary and analogue input patterns. However, Fuzzy ART has the cluster overlapping problem. In this study, to solve this problem, we propose a new improved fuzzy ART (IFART) algorithm. In the proposed algorithm, after the clusters are formed, membership degrees of each data instance to all clusters are calculated according to the cluster centers. If data instances are not in the cluster with maximum membership degree, then they are moved between clusters according to their maximum membership degrees. The clustering results on real sample datasets are investigated and compared with the conventional Fuzzy ART. It is seen that, Improved Fuzzy ART is more efficient then Fuzzy ART and also a high performance algorithm than SOM.

Keywords

ART neural nets; fuzzy set theory; pattern clustering; unsupervised learning; SOM; adaptive resonance theory; cluster centers; cluster overlapping problem; fuzzy clustering; improved fuzzy ART algorithm; unsupervised neural network; Artificial intelligence; Clustering algorithms; Corporate acquisitions; Fuzzy logic; Fuzzy neural networks; Intelligent systems; Neural networks; Resonance; Subspace constraints; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, 2009. ICSCCW 2009. Fifth International Conference on

Conference_Location

Famagusta

Print_ISBN

978-1-4244-3429-9

Electronic_ISBN

978-1-4244-3428-2

Type

conf

DOI

10.1109/ICSCCW.2009.5379445

Filename

5379445