Linear assignment clustering algorithm based on the least similar cluster representatives

Jun Wang

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

This 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.

Original languageEnglish
Pages (from-to)3552-3557
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume4
Publication statusPublished - 1997
Externally publishedYes
EventProceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5) - Orlando, FL, USA
Duration: 12 Oct 199715 Oct 1997

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