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

Research output: Journal Publications and ReviewsRGC 22 - Publication in policy or professional journal

22 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)100-104
Journal / PublicationIEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans
Volume29
Issue number1
Publication statusPublished - 1999
Externally publishedYes

Abstract

This correspondence presents a linear assignment algorithm for solving the 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. © 1999 IEEE.

Research Area(s)

  • Assignment problem, Clustering algorithm, Group technology