LINEX K-Means: Clustering by an Asymmetric Dissimilarity Measure

DOI

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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TY  - JOUR
AU  - Narges Ahmadzadehgoli
AU  - Adel Mohammadpour
AU  - Mohammad Hassan Behzadi
PY  - 2018
DA  - 2018/03/31
TI  - LINEX K-Means: Clustering by an Asymmetric Dissimilarity Measure
JO  - Journal of Statistical Theory and Applications
SP  - 29
EP  - 38
VL  - 17
IS  - 1
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.2018.17.1.3
DO  - 10.2991/jsta.2018.17.1.3
ID  - Ahmadzadehgoli2018
ER  -