The LINEX Weighted k-Means Clustering

DOI

10.2991/jsta.d.190524.001How to use a DOI?

Keywords

LINEX loss function; Feature weights; Weighted k-means; Clustering

Abstract

LINEX weighted k-means is a version of weighted k-means clustering, which computes the weights of features in each cluster automatically. Determining which entity is belonged to which cluster depends on the cluster centers. In this study, the asymmetric LINEX loss function is used to compute the dissimilarity in the weighted k-means clustering. So, the cluster centroids are obtained by minimizing a LINEX based cost function. This loss function is used as a dissimilarity measure in clustering when one wants to overestimate or underestimate the cluster centroids, which helps to reduce some errors of misclassifying entities. Therefore, we discuss the LINEX weighted k-means algorithm. We examine the accuracy of the algorithm with some synthetic and real datasets.

Copyright

© 2019 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Narges Ahmadzadehgoli
AU  - Adel Mohammadpour
AU  - Mohammad Hassan Behzadi
PY  - 2019
DA  - 2019/05/30
TI  - The LINEX Weighted k-Means Clustering
JO  - Journal of Statistical Theory and Applications
SP  - 147
EP  - 154
VL  - 18
IS  - 2
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.d.190524.001
DO  - 10.2991/jsta.d.190524.001
ID  - Ahmadzadehgoli2019
ER  -