Linear assignment clustering algorithm based on the least similar cluster representatives
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 3552-3557 |
Journal / Publication | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics |
Volume | 4 |
Publication status | Published - 1997 |
Externally published | Yes |
Conference
Title | Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5) |
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City | Orlando, FL, USA |
Period | 12 - 15 October 1997 |
Link(s)
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.
Citation Format(s)
Linear assignment clustering algorithm based on the least similar cluster representatives. / Wang, Jun.
In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Vol. 4, 1997, p. 3552-3557.
In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Vol. 4, 1997, p. 3552-3557.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review