Journal of Statistical Theory and Applications

Volume 17, Issue 1, March 2018, Pages 29 - 38

LINEX K-Means: Clustering by an Asymmetric Dissimilarity Measure

Authors
Narges Ahmadzadehgolin_ah652@yahoo.com
Department of Statistics, Science and Research Branch, Islamic Azad University, Tehran, Iran
Adel Mohammadpour1adel@aut.ac.ir
Department of Statistics, Faculty of Mathematics & Computer Science, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Ave., Tehran, Iran.
Mohammad Hassan Behzadibehzadi@srbiau.ac.ir
Department of Statistics, Science and Research Branch, Islamic Azad University, Tehran, Iran
Corresponding Author
Narges Ahmadzadehgolin_ah652@yahoo.com
Received 5 October 2016, Accepted 6 March 2017, Available online 1 January 2018.
DOI
https://doi.org/10.2991/jsta.2018.17.1.3How to use a DOI?
Keywords
LINEX loss function, dissimilarity measure, k-means clustering
Abstract
Clustering is a well-known approach in data mining, which is used to separate data without being labeled. Some clustering methods are more popular such as the k-means. In all clustering techniques, the cluster centers must be found that help to determine which object is belonged to which cluster by measuring the dissimilarity measure. We choose the dissimilarity measure, according to the construction of the data. When the overestimation and the underestimation are not equally important, an asymmetric dissimilarity measure is appropriate. So, we discuss the asymmetric LINEX loss function as a dissimilarity measure in k-means clustering algorithm instead of the squared Euclidean. We evaluate the algorithm results with some simulated and real datasets.
Copyright
Copyright © 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).


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@article{Ahmadzadehgoli2018,
  title={LINEX K-Means: Clustering by an Asymmetric Dissimilarity Measure},
  author={Ahmadzadehgoli, Narges and Mohammadpour, Adel and Behzadi, Mohammad Hassan},
  year={2018},
  journal={Journal of Statistical Theory and Applications},
  volume={17},
  issue={1},
  pages={29-38},
  issn={1538-7887},
  url={https://doi.org/10.2991/jsta.2018.17.1.3},
  doi={https://doi.org/10.2991/jsta.2018.17.1.3}
}
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