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Sweep-Hyperplane Clustering Algorithm Using Dynamic Model
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DOI：10.15388/Informatica.2014.30.png)
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Abstract
En 中文
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 2(N)-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