title: Robust Clustering Method in the Presence of Scattered Observations.
abstract: Contamination of scattered observations, which are either featureless or unlike the other observations, frequently degrades the performance of standard methods such as K-means and model-based clustering. In this letter, we propose a robust clustering method in the presence of scattered observations called Gamma-clust. Gamma-clust is based on a robust estimation for cluster centers using gamma-divergence. It provides a proper solution for clustering in which the distributions for clustered data are nonnormal, such as t-distributions with different variance-covariance matrices and degrees of freedom. As demonstrated in a simulation study and data analysis, Gamma-clust is more flexible and provides superior results compared to the robustified K-means and model-based clustering.
authors.affiliation: Department of Health Sciences, Oita University of Nursing and Health Sciences, Megusuno, Oita 870-1201, Japan notsu@oita-nhs.ac.jp.
authors.forename: Akifumi
authors.initials: A
authors.lastname: Notsu
authors.affiliation: Institute of Statistical Mathematics and Graduate University for Advanced Studies, Tachikawa, Tokyo 190-8562, Japan eguchi@ism.ac.jp.
authors.forename: Shinto
authors.initials: S
authors.lastname: Eguchi