A new framework of fuzzy clustering algorithm

Author

Shieh, Horng-lin

Author_Institution

Dept. of Electr. Eng., St. John´´s Univ., Taipei, Taiwan

fYear

2011

fDate

27-30 June 2011

Firstpage

2833

Lastpage

2838

Abstract

In this paper, a novel data clustering algorithm based on the subtractive clustering (SC) algorithm and a new validity index are proposed. The SC algorithm is a simple method for data clustering; however, it has two problems which must be overcome. The first problem is such that the cluster centers found by SC are taken from data with the highest potential values, but that this data may not be the real cluster centers. The second problem is such that the cluster number generated by the SC algorithm is influenced by a predefined parameter. The proposed algorithm is based on distance relations between data and centers and is designed to ascertain the real centers generated by the SC algorithm. In addition, a novel robust cluster index is proposed to identify the real cluster number generated by SC algorithm.

Keywords

fuzzy set theory; pattern clustering; cluster centers; data clustering; fuzzy clustering algorithm; robust cluster index; subtractive clustering algorithm; validity index; Algorithm design and analysis; Clustering algorithms; Equations; Indexes; Nickel; Noise; Partitioning algorithms; clustering algorithm; subtractive clustering (SC) algorithm; validity index;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems (FUZZ), 2011 IEEE International Conference on

Conference_Location

Taipei

ISSN

1098-7584

Print_ISBN

978-1-4244-7315-1

Electronic_ISBN

1098-7584

Type

conf

DOI

10.1109/FUZZY.2011.6007370

Filename

6007370