The LINEX Weighted k-Means Clustering

被引：0
|
作者：
Narges Ahmadzadehgoli
Adel Mohammadpour
Mohammad Hassan Behzadi
机构：
[1] Islamic Azad University,Department of Statistics, Science and Research Branch
[2] Amirkabir University of Technology (Tehran Polytechnic),Department of Statistics, Faculty of Mathematics & Computer Science
来源：
Journal of Statistical Theory and Applications | 2019年 / 18卷
关键词：
LINEX loss function; Feature weights; Weighted k-means; Clustering;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：147 / 154
页数：7
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