DocumentCode
1454413
Title
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
29
Issue
1
fYear
1999
fDate
1/1/1999 12:00:00 AM
Firstpage
100
Lastpage
104
Abstract
This paper presents a linear assignment algorithm for solving the clustering problem. By using the most dissimilar data as cluster representatives, a linear assignment algorithm is developed based on the 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
data analysis; pattern recognition; production control; cluster representatives; group technology; least similar data; linear assignment clustering; linear assignment model; multivariate data analysis; Clustering algorithms; Clustering methods; Data analysis; Data engineering; Group technology; Manufacturing systems; Neural networks; Optimization methods; Resonance; Search methods;
fLanguage
English
Journal_Title
Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on
Publisher
ieee
ISSN
1083-4427
Type
jour
DOI
10.1109/3468.736364
Filename
736364
Link To Document