A modified density-based clustering algorithm and its implementation

Zhihua Ban; Jianguo Liu; Lulu Yuan; Hua Yang

doi:10.1117/12.2204778

Translator Disclaimer

14 December 2015 A modified density-based clustering algorithm and its implementation

Zhihua Ban, Jianguo Liu, Lulu Yuan, Hua Yang

Author Affiliations +

Proceedings Volume 9813, MIPPR 2015: Pattern Recognition and Computer Vision; 98130E (2015) https://doi.org/10.1117/12.2204778
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

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.

Citation Download Citation

Zhihua Ban, Jianguo Liu, Lulu Yuan, and Hua Yang "A modified density-based clustering algorithm and its implementation", Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130E (14 December 2015); https://doi.org/10.1117/12.2204778

ACCESS THE FULL ARTICLE