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.
|Number of pages||6|
|Journal||Proceedings of the IEEE International Conference on Systems, Man and Cybernetics|
|Publication status||Published - 1997|
|Event||Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5) - Orlando, FL, USA|
Duration: 12 Oct 1997 → 15 Oct 1997