A density-based enhancement to dominant sets clustering

Although there is no shortage of clustering algorithms, existing algorithms are often afflicted by problems of one kind or another. Dominant sets clustering is a graph-theoretic approach to clustering and exhibits significant potential in various applications. However, the authors' work indicat...

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Bibliographic Details
Published in:IET computer vision 2013-10, Vol.7 (5), p.354-361
Main Authors: Hou, Jian, Xu, E, Liu, Wei-Xue, Xia, Qi, Qi, Nai-Ming
Format: Article
Language:English
Subjects:
adjacent cluster fusion
Algorithms
Applied sciences
cluster merging step
density-based enhancement
distance measure sensitiveness
dominant sets clustering
Exact sciences and technology
graph theory
graph-theoretic approach
Information, signal and communications theory
over-segmentation tendency
pattern clustering
set theory
Signal and communications theory
Signal representation. Spectral analysis
Signal, noise
Telecommunications and information theory
unsupervised learning
unsupervised learning tools
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Summary:Although there is no shortage of clustering algorithms, existing algorithms are often afflicted by problems of one kind or another. Dominant sets clustering is a graph-theoretic approach to clustering and exhibits significant potential in various applications. However, the authors' work indicates that this approach suffers from two major problems, namely over-segmentation tendency and sensitiveness to distance measures. In order to overcome these two problems, the authors present a density-based enhancement to dominant sets clustering where a cluster merging step is used to fuse adjacent clusters close enough from the original dominant sets clustering. Experiments on various datasets validate the effectiveness of the proposed method.
ISSN:1751-9632
1751-9640
1751-9640
DOI:10.1049/iet-cvi.2013.0072