Robust clustering based on global data distribution and local connectivity matrix

Author

Qian, Yun-tao ; Zhao, Rong-chun

Author_Institution

Dept. of Comput. Sci. & Eng., Northwestern Polytech. Univ., Xian, China

Volume

2

fYear

1997

fDate

28-31 Oct 1997

Firstpage

1629

Abstract

A new method of clustering analysis, which is based on integration of graph theoretical method and fuzzy objective function algorithm, is developed. The connectivity matrix derived from fuzzy limited neighborhood graph and the measurement for similarity and dissimilarity are utilized to build a new fuzzy objective function that unifies global data distribution and local spatial information. In some sense, both the traditional graph theoretical method and objective function algorithm are special cases of our algorithm

Keywords

data analysis; fuzzy set theory; graph theory; matrix algebra; pattern recognition; clustering analysis; fuzzy limited neighborhood graph; fuzzy objective function algorithm; global data distribution; graph theoretical method; local connectivity matrix; local spatial information; objective function algorithm; robust clustering; traditional graph theoretical method; Algorithm design and analysis; Clustering algorithms; Data structures; Distance measurement; Ellipsoids; Functional programming; Fuzzy control; Fuzzy set theory; Kernel; Partitioning algorithms; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Processing Systems, 1997. ICIPS '97. 1997 IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-4253-4

Type

conf

DOI

10.1109/ICIPS.1997.669317

Filename

669317