1. Sweep-Hyperplane Clustering Algorithm Using Dynamic Model.
- Author
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LUKAČ, Niko, ŽALIK, Borut, and ŽALIK, Krista Rizman
- Subjects
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HYPERPLANES , *ALGORITHMS , *DYNAMIC models , *CLUSTER analysis (Statistics) , *APPROXIMATION theory - Abstract
Clustering is one of the better known unsupervised learning methods with the aim of discovering structures in the data. This paper presents a distance-based Sweep-Hyperplane Clustering Algorithm (SHCA), which uses sweep-hyperplanes to quickly locate each point's approximate nearest neighbourhood. Furthermore, a new distance-based dynamic model that is based on 2N-tree hierarchical space partitioning, extends SHCA's capability for finding clusters that are not well-separated, with arbitrary shape and density. Experimental results on different synthetic and real multidimensional datasets that are large and noisy demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2014
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