A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2012; you can also visit the original URL.
The file type is application/pdf.
A linear assignment clustering algorithm based on the least similar cluster representatives
1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation
Preserved Fulltext
This paper presents a linear assignment algorithm for solviny the classical NP-complete clustering problem. By use of the most dissimilar data us 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 eficient, especially for clustering a large number of data with many attributes.
doi:10.1109/icsmc.1997.633206
fatcat:tzfpurcgnrehlbpf4sjwbk2h44