Journal
INFORMATION SCIENCES
Volume 360, Issue -, Pages 231-243Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2016.04.032
Keywords
Geodesic distance; Mutual neighborhood-based density coefficient; Noisy data clustering; Nonlinearity
Categories
Funding
- Brain Korea PLUS
- Basic Science Research Program through the National Research Foundation of Korea - Ministry of Science, ICT and Future Planning [2013007724]
- National Science Foundation [CCF-1144502]
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1144502] Funding Source: National Science Foundation
Ask authors/readers for more resources
Clustering analysis can facilitate the extraction of implicit patterns in a dataset and elicit its natural groupings without requiring prior classification information. For superior clustering analysis results, a number of distance measures have been proposed. Recently, geodesic distance has been widely applied to clustering algorithms for nonlinear groupings. However, geodesic distance is sensitive to noise and hence, geodesic distance-based clustering may fail to discover nonlinear clusters in the region of the noise. In this study, we propose a density-based geodesic distance that can identify clusters in nonlinear and noisy situations. Experiments on various simulation and benchmark datasets are conducted to examine the properties of the proposed geodesic distance and to compare its performance with that of existing distance measures. The experimental results confirm that a clustering algorithm with the proposed distance measure demonstrated superior performance compared to the competitors; this was especially true when the cluster structures in the data were inherently noisy and nonlinearly patterned. (C) 2016 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available