A density‐based enhancement to dominant sets clustering. Issue 5 (1st October 2013)

• Record Type: Journal Article
• Authors: Hou, Jian; Xu, E; Liu, Wei‐Xue; Xia, Qi; Qi, Nai‐Ming
• Is Part Of: IET computer vision. Volume 7:Issue 5(2013)
• Page Start: 354
• Page End: 361
• Publication Date: 2013-10-01
• Subjects: graph theory -- pattern clustering -- set theory -- unsupervised learning; unsupervised learning tools -- adjacent cluster fusion -- cluster merging step -- distance measure sensitiveness -- over-segmentation tendency -- graph-theoretic approach -- dominant sets clustering -- density-based enhancement; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2013.0072
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : 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.