LINEX K-Means: Clustering by an Asymmetric Dissimilarity Measure
- 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 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 -