The LINEX Weighted k-Means Clustering
- 10.2991/jsta.d.190524.001How to use a DOI?
- LINEX loss function; Feature weights; Weighted k-means; Clustering
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.
- © 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/).
Download article (PDF)
View full text (HTML)
Cite this article
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 -