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A New Clustering Approach for Partioning Directional Data
Citation key Klose2005
Author Klose, C. and Seo, S. and Obermayer, K.
Pages 315 – 321
Year 2005
Journal Int. J. Rock Mech. Mining Sci.
Volume 42
Abstract We present a new clustering approach for the partioning of directional data, which is based on vector quantization. Directional data are grouped into disjoint isotropic clusters and - at the same time - the average dip direction and the average dip angle are calculated for each group. The method is based on a completly new and mathematically self-consistent approach. Grouping is achieved by minimizing the average distance between the data points and the average values which characterize the cluster to which the data points belong. The distance between directional data is measured by the arc-length between the corresponding poles on the unit sphere. The algorithm is fast and shows good clustering results compared to the counting method of Shanley and Mahtab and the expert-supervised grouping methods developed by Pecher. No heuristics is being used, because the grouping of data points, the assignment of new data points to clusters, and the calculation of the average cluster values are based on the same cost function. The new method minimizes manual interactions and does not require the calculation of a contour density plot. In ongoing research investigations, the new approach will be extended to probabilistic assignments (soft-clustering), and to grouping problems which involve anisotropic clusters.
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