Constrained K-Means Classification

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Constrained K-Means Classification

Journal Title: Engineering, Technology & Applied Science Research - Year 2018, Vol 8, Issue 4

Abstract

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.

Authors and Affiliations

P. N. Smyrlis, D. C. Tsouros, M. G. Tsipouras

Keywords

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

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EP ID EP229884
DOI -
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