VoCC: Vortex Correlation Clustering Based on Masked Hough Transformation in Spatial Databases

A special focus in data mining is to identify agglomerations of data points in spatial or spatio-temporal databases. Multiple applications have been presented to make use of such clustering algorithms. However, applications exist, where not only dense areas have to be identified, but also requirements regarding the correlation of the cluster to a specific shape must be met, i.e. circles. This is the case for eddy detection in marine science, where eddies are not only specified by their density, but also their circular-shaped rotation. Traditional clustering algorithms lack the ability to take such aspects into account. In this paper, we introduce Vortex Correlation Clustering which aims to identify those correlated groups of objects oriented along a vortex. This can be achieved by adapting the Circle Hough Transformation, already known from image analysis. The presented adaptations not only allow to cluster objects depending on their location next to each other, but also allows to take the orientation of individual objects into considerations. This allows for a more precise clustering of objects. A multi-step approach allows to analyze and aggregate cluster candidates, to also include final clusters, which do not perfectly satisfy the shape condition. We evaluate our approach upon a real world application, to cluster particle simulations composing such shapes. Our approach outperforms comparable methods of clustering for this application both in terms of effectiveness and efficiency.