Sweep-Hyperplane Clustering Algorithm Using Dynamic Model

Article type: Research Article

Authors: Lukač, Niko^{; *} | Žalik, Borut | Žalik, Krista Rizman

Affiliations: Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ulica 17, SI-2000 Maribor, Slovenia, e-mail: [email protected]

Correspondence: [*] Corresponding author.

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.

Keywords: clustering, sweeping paradigm, dynamic model

Journal: Informatica, vol. 25, no. 4, pp. 563-580, 2014