Volume 19, Issue 1, March 2020, Pages 91 - 101
Fuzzy C-Means Clustering Using Asymmetric Loss Function
Authors
Israa Abdzaid Atiyah1, Adel Mohammadpour1, *, Narges Ahmadzadehgoli2, S. Mahmoud Taheri3
1Faculty of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran
2Telecommunication of Iran Company, Tehran, Iran
3School of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran
*Corresponding author. Email: adel@aut.ac.ir
Corresponding Author
Adel Mohammadpour
Received 26 November 2018, Accepted 15 February 2019, Available Online 9 March 2020.
- DOI
- 10.2991/jsta.d.200302.002How to use a DOI?
- Keywords
- Fuzzy C-Means clustering; LINEX loss function
- Abstract
In this work, a fuzzy clustering algorithm is proposed based on the asymmetric loss function instead of the usual symmetric dissimilarities. Linear Exponential (LINEX) loss function is a commonly used asymmetric loss function, which is considered in this paper. We prove that the negative likelihood of an extreme value distribution is equal to LINEX loss function and clarify some of its advantages. Using such a loss function, the so-called LINEX Fuzzy C-Means algorithm is introduced. The introduced clustering method is compared with its crisp version and Fuzzy C-Means algorithms through a few real datasets as well as some simulated datasets.
- Copyright
- © 2020 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 - Israa Abdzaid Atiyah AU - Adel Mohammadpour AU - Narges Ahmadzadehgoli AU - S. Mahmoud Taheri PY - 2020 DA - 2020/03/09 TI - Fuzzy C-Means Clustering Using Asymmetric Loss Function JO - Journal of Statistical Theory and Applications SP - 91 EP - 101 VL - 19 IS - 1 SN - 2214-1766 UR - https://doi.org/10.2991/jsta.d.200302.002 DO - 10.2991/jsta.d.200302.002 ID - Atiyah2020 ER -