Provided by: RWTH Aachen University
Topic: Data Management
Circular data, i.e., data in the form of 'Natural' directions or angles are very common in a number of different areas such as biological, meteorological, geological and political sciences. Clustering circular data is not an easy task due to the circular geometry of the data space. Some clustering approaches, such as the spherical k-means, use the cosine distance instead of the Euclidean distance in order to measure the difference between points. In this paper, the authors propose a variation of the randomized gravitational clustering algorithm in order to deal with circular data.