Narges Ahmadzadehgoli, Adel Mohammadpour, Mohammad Hassan Behzadi
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.