Estimation of the number of clusters on d-dimensional sphere
This addresses a specific issue in analyzing spherical data for researchers in domains like meteorology and biology, but it appears incremental as it adapts an existing method to a spherical context.
The paper tackles the problem of estimating the number of clusters in spherical data, which appears in fields like meteorology and NLP, by proposing a new method called Spherical X-means (SX-means) that assumes a mixture of von Mises-Fisher distributions, and it demonstrates the method's performance in this estimation task.
Spherical data is distributed on the sphere. The data appears in various fields such as meteorology, biology, and natural language processing. However, a method for analysis of spherical data does not develop enough yet. One of the important issues is an estimation of the number of clusters in spherical data. To address the issue, I propose a new method called the Spherical X-means (SX-means) that can estimate the number of clusters on d-dimensional sphere. The SX-means is the model-based method assuming that the data is generated from a mixture of von Mises-Fisher distributions. The present paper explains the proposed method and shows its performance of estimation of the number of clusters.