An Adaptive Cluster Validity Index Based on Fuzzy Set

Author

Duo, Chen ; Tie-Jun, Zhang ; Li-Fen, Zhao

Author_Institution

Dept. of Comput. Sci. & Technol., Tangshan Coll., Tangshan, China

fYear

2012

Firstpage

1824

Lastpage

1827

Abstract

Based on the basic theory of fuzzy set, this paper proposes the notion of FCM fuzzy set, which is subject to the constraint condition of FCM algorithm. The cluster fuzzy degree and the lattice degree of approaching for the FCM fuzzy set are presented, and their functions in the validation process of fuzzy clustering are deeply analyzed. A new cluster validity index is proposed, in which two factors such as the cluster fuzzy degree and the lattice degree of approaching are taken into comprehensive account. The notable advantage of the index is that it can adaptively adjust the relative significance levels of the two factors. The experimental results indicate the effectiveness and adaptability of the proposed cluster validity index.

Keywords

data mining; fuzzy set theory; pattern clustering; FCM algorithm; FCM fuzzy set; adaptive cluster validity index; cluster fuzzy degree; data mining; fuzzy clustering analysis; fuzzy set theory; knowledge discovery; lattice degree; validation process; Algorithm design and analysis; Clustering algorithms; Fuzzy sets; Indexes; Iris; Lattices; Partitioning algorithms; Cluster Analysis; Cluster Validity Index; FCM; Fuzzy Set;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Control and Electronics Engineering (ICICEE), 2012 International Conference on

Conference_Location

Xi´an

Print_ISBN

978-1-4673-1450-3

Type

conf

DOI

10.1109/ICICEE.2012.483

Filename

6322773