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Proceedings Paper

A fast randomized clustering method based on a hypothetical potential field
Author(s): Yonggang Lu; Li Liao; Ruhai Wang
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Paper Abstract

A novel randomized clustering method is proposed to overcome some of the drawbacks of Mean Shift method. A hypothetical potential field is constructed from all the data points. Different from Mean Shift which moves the kernel window towards high-density region, our method moves the kernel window towards low-potential region. The proposed method is evaluated by comparing with both Mean Shift and K-means++ on three synthetic data sets which represent the clusters of different sizes, different shapes and different distributions. The experiments show that our method can produce more accurate results than both Mean Shift and K-means++.

Paper Details

Date Published: 2 December 2011
PDF: 6 pages
Proc. SPIE 8004, MIPPR 2011: Pattern Recognition and Computer Vision, 800403 (2 December 2011); doi: 10.1117/12.902696
Show Author Affiliations
Yonggang Lu, Lanzhou Univ. (China)
Li Liao, Lanzhou Univ. (China)
Ruhai Wang, Lamar Univ. (United States)


Published in SPIE Proceedings Vol. 8004:
MIPPR 2011: Pattern Recognition and Computer Vision
Jonathan Roberts; Jie Ma, Editor(s)

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