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Sweep-Hyperplane Clustering Algorithm Using Dynamic Model
Volume 25, Issue 4 (2014), pp. 563–580
Niko Lukač   Borut Žalik   Krista Rizman Žalik  

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https://doi.org/10.15388/Informatica.2014.30
Pub. online: 1 January 2014      Type: Article     

Received
1 May 2013
Accepted
1 March 2014
Published
1 January 2014

Abstract

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.

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Vilnius University

Keywords
clustering sweeping paradigm dynamic model

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INFORMATICA

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