Volume 17, Issue 1, March 2018, Pages 29 - 38
LINEX K-Means: Clustering by an Asymmetric Dissimilarity Measure
- Narges Ahmadzadehgoli, firstname.lastname@example.orgDepartment of Statistics, Science and Research Branch, Islamic Azad University, Tehran, IranAdel Mohammadpour1, email@example.comDepartment of Statistics, Faculty of Mathematics & Computer Science, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Ave., Tehran, Iran.Mohammad Hassan Behzadi, firstname.lastname@example.orgDepartment of Statistics, Science and Research Branch, Islamic Azad University, Tehran, Iran1Corresponding author. Tel.: +98 2164542533; Fax: +98 2166497930; E-mail: email@example.com.
- Corresponding Author
- Narges Ahmadzadehgolin_ah652@yahoo.com
Received 5 October 2016, Accepted 6 March 2017, Available Online March 2018.
- https://doi.org/10.2991/jsta.2018.17.1.3How to use a DOI?
- LINEX loss function, dissimilarity measure, k-means clustering
- 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 © 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/).
Cite this article
TY - JOUR AU - Narges Ahmadzadehgoli AU - Adel Mohammadpour AU - Mohammad Hassan Behzadi PY - 2018/03 DA - 2018/03 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 - 1538-7887 UR - https://doi.org/10.2991/jsta.2018.17.1.3 DO - https://doi.org/10.2991/jsta.2018.17.1.3 ID - Ahmadzadehgoli2018/03 ER -