RT Journal Article
A1 Jian Hou
AD School of Information Science and Technology, Bohai University, Jinzhou, Liaoning 121013, People's Republic of China
A1 E Xu
AD School of Information Science and Technology, Bohai University, Jinzhou, Liaoning 121013, People's Republic of China
AD China Centre for Industrial Security Research, Beijing Jiaotong University, Beijing 100044, People's Republic of China
A1 Wei-Xue Liu
AD School of Information Science and Technology, Bohai University, Jinzhou, Liaoning 121013, People's Republic of China
A1 Qi Xia
AD School of Astronautics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, People's Republic of China
A1 Nai-Ming Qi
AD School of Astronautics, Harbin Institute of Technology, Harbin, Heilongjiang 150001, People's Republic of China

PB iet
T1 A density-based enhancement to dominant sets clustering
JN IET Computer Vision
VO 7
IS 5
SP 354
OP 361
AB 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.
K1 density-based enhancement
K1 distance measure sensitiveness
K1 adjacent cluster fusion
K1 unsupervised learning tools
K1 over-segmentation tendency
K1 cluster merging step
K1 dominant sets clustering
K1 graph-theoretic approach
DO https://doi.org/10.1049/iet-cvi.2013.0072
UL https://digital-library.theiet.org/;jsessionid=12gin3yqh5bit.x-iet-live-01content/journals/10.1049/iet-cvi.2013.0072
LA English
SN 1751-9632
YR 2013
OL EN