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

Authors:
Jun Wang
Affiliations:
Dept. of Mech. & Autom. Eng., Chinese Univ. of Hong Kong, Shatin
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Year:
1999

Citing 0
Cited 4

Associative Clustering for Clusters of Arbitrary Distribution Shapes

Neural Processing Letters
A novel similarity measure for data clustering

Intelligent Data Analysis
A numerical comparison between simulated annealing and evolutionary approaches to the cell formation problem

Expert Systems with Applications: An International Journal
Application of visual clustering properties of self organizing map in machine-part cell formation

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

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