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A note on constrained k-means algorithms
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DOI：10.1016/S0031-3203(99)00057-6.png)
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Abstract
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This paper describes extensions to the k-means algorithm for clustering data sets. By adding suitable constraints into the mathematical program formulation, an approach is developed, which allows the use of the ii-means paradigm to efficiently cluster data sets with the fixed number of objects in each cluster. The new algorithm is presented and the effectiveness of the algorithm is demonstrated with experimental results. (C) 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Keywords:
clustering
constraints
k-means algorithm
PCB insertion
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