Constrained K-Means Classification


  • P. N. Smyrlis Department of Informatics and Telecommunications Engineering, University of Western Macedonia, Greece
  • D. C. Tsouros Department of Informatics and Telecommunications Engineering, University of Western Macedonia, Greece
  • M. G. Tsipouras Department of Informatics and Telecommunications Engineering, University of Western Macedonia, Greece
Volume: 8 | Issue: 4 | Pages: 3203-3208 | August 2018 |


Classification-via-clustering (CvC) is a widely used method, using a clustering procedure to perform classification tasks. In this paper, a novel K-Means-based CvC algorithm is presented, analysed and evaluated. Two additional techniques are employed to reduce the effects of the limitations of K-Means. A hypercube of constraints is defined for each centroid and weights are acquired for each attribute of each class, for the use of a weighted Euclidean distance as a similarity criterion in the clustering procedure. Experiments are made with 42 well–known classification datasets. The experimental results demonstrate that the proposed algorithm outperforms CvC with simple K-Means.


classification-via-clustering, k-means, supervised learning


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How to Cite

P. N. Smyrlis, D. C. Tsouros, and M. G. Tsipouras, “Constrained K-Means Classification”, Eng. Technol. Appl. Sci. Res., vol. 8, no. 4, pp. 3203–3208, Aug. 2018.


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