Title :
A linear assignment clustering algorithm based on the least similar cluster representatives
Author_Institution :
Dept. of Mech. & Autom. Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong
fDate :
1/1/1999 12:00:00 AM
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;
Journal_Title :
Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on
DOI :
10.1109/3468.736364
