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Proceedings Paper

A modified density-based clustering algorithm and its implementation
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Paper Abstract

This paper presents an improved density-based clustering algorithm based on the paper of clustering by fast search and find of density peaks. A distance threshold is introduced for the purpose of economizing memory. In order to reduce the probability that two points share the same density value, similarity is utilized to define proximity measure. We have tested the modified algorithm on a large data set, several small data sets and shape data sets. It turns out that the proposed algorithm can obtain acceptable results and can be applied more wildly.

Paper Details

Date Published: 14 December 2015
PDF: 8 pages
Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130E (14 December 2015); doi: 10.1117/12.2204778
Show Author Affiliations
Zhihua Ban, Huazhong Univ. of Science and Technology (China)
Jianguo Liu, Huazhong Univ. of Science and Technology (China)
Lulu Yuan, Huazhong Univ. of Science and Technology (China)
Hua Yang, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 9813:
MIPPR 2015: Pattern Recognition and Computer Vision
Tianxu Zhang; Jianguo Liu, Editor(s)

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