A0603
Title: On model-based clustering of directional data with heavy tails and scatter
Authors: Volodymyr Melnykov - The University of Alabama (United States)
Igor Melnykov - University of Minnesota Duluth (United States)
Yingying Zhang - Western Michigan Univesity (United States) [presenting]
Abstract: Directional statistics deals with data that can be naturally expressed in the form of vector directions. Von Mises-Fisher distribution is one of the most fundamental parametric models to describe directional data. Mixtures of von Mises-Fisher distributions represent a popular approach to handling heterogeneous populations. However, such models can be affected by the presence of noise, outliers, and heavy tails. To relax these model limitations, a mixture of contaminated von Mises-Fisher distributions is proposed. The performance of the proposed methodology is tested on synthetic data and applied to the data containing abstracts from the Joint Statistical Meetings held in Denver in 2008. The obtained results demonstrate the importance of the proposed procedure and its superiority over the traditional mixture of von Mises-Fisher distributions in the cases of heavy tails or scatter.
