A linear assignment clustering algorithm based on the least similar cluster representatives
Research output: Journal Publications and Reviews › RGC 22 - Publication in policy or professional journal
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 100-104 |
Journal / Publication | IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans |
Volume | 29 |
Issue number | 1 |
Publication status | Published - 1999 |
Externally published | Yes |
Link(s)
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
Citation Format(s)
A linear assignment clustering algorithm based on the least similar cluster representatives. / Wang, Jun.
In: IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, Vol. 29, No. 1, 1999, p. 100-104.
In: IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, Vol. 29, No. 1, 1999, p. 100-104.
Research output: Journal Publications and Reviews › RGC 22 - Publication in policy or professional journal