TY - JOUR
T1 - Linear assignment clustering algorithm based on the least similar cluster representatives
AU - Wang, Jun
PY - 1997
Y1 - 1997
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0031361451&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0031361451&origin=recordpage
U2 - 10.1109/ICSMC.1997.633206
DO - 10.1109/ICSMC.1997.633206
M3 - RGC 21 - Publication in refereed journal
SN - 0884-3627
VL - 4
SP - 3552
EP - 3557
JO - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
JF - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
T2 - Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5)
Y2 - 12 October 1997 through 15 October 1997
ER -