Back to Search
Start Over
Sweep-Hyperplane Clustering Algorithm Using Dynamic Model.
- Source :
-
Informatica . 2014, Vol. 25 Issue 4, p563-580. 18p. - Publication Year :
- 2014
-
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]
Details
- Language :
- English
- ISSN :
- 08684952
- Volume :
- 25
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Informatica
- Publication Type :
- Academic Journal
- Accession number :
- 108796450
- Full Text :
- https://doi.org/10.15388/Informatica.2014.30