A linear assignment clustering algorithm based on the least similar cluster representatives

Author

Wang, Jun

Author_Institution

Dept. of Mech. & Autom. Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong

Volume

4

fYear

1997

fDate

12-15 Oct 1997

Firstpage

3552

Abstract

The paper presents a linear assignment algorithm for solving the classical NP complete clustering problem. By use of the most dissimilar data as cluster representatives, a linear assignment algorithm is developed based on a linear assignment model for clustering multivariate data. The computational results evaluated using multiple performance criteria show that the clustering algorithm is very effective and efficient, especially for clustering a large number of data with many attributes

Keywords

computational complexity; data handling; pattern recognition; classical NP complete clustering problem; least similar cluster representatives; linear assignment clustering algorithm; linear assignment model; most dissimilar data; multiple performance criteria; multivariate data clustering; Automation; Clustering algorithms; Data analysis; Data engineering; Group technology; Manufacturing systems; Neural networks; Optimization methods; Resonance; Search methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on

Conference_Location

Orlando, FL

ISSN

1062-922X

Print_ISBN

0-7803-4053-1

Type

conf

DOI

10.1109/ICSMC.1997.633206

Filename

633206