Share Email Print
cover

Proceedings Paper

Clustering by exponential density analysis and find of cluster centers based on genetic algorithm
Author(s): Dong Kun; Wang Ze; Zhang Rui; Yin Chao
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Finding the optimal solution to the problem of selecting clustering centers and improving the performance of existing density-based clustering algorithms, a novel clustering method is proposed in this paper. Our algorithm discovers data clusters according to cluster centers that are identified by a higher density than their nearby points and by a comparatively large distance from points with higher density, and then it finds optimal cluster centers by iteration based on genetic algorithm. We present an exponential density analysis to reduce the impact of model parameters and introduce a penalty factor in order to overcome the excursion of search region for accelerating convergence. Experiments on both artificial and UCI data sets reveal that our algorithm achieves results on Rand Statistic competitive with a variety of classical algorithms.

Paper Details

Date Published: 29 August 2016
PDF: 6 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 1003362 (29 August 2016); doi: 10.1117/12.2244868
Show Author Affiliations
Dong Kun, PLA Univ. of Science and Technology (China)
Wang Ze, PLA Univ. of Science and Technology (China)
Zhang Rui, PLA Univ. of Science and Technology (China)
Yin Chao, PLA Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

© SPIE. Terms of Use
Back to Top