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A modified density-based clustering algorithm and its implementation

A modified density-based clustering algorithm and its implementation 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings of SPIE SPIE

A modified density-based clustering algorithm and its implementation

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Publisher
SPIE
Copyright
COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
ISSN
0277-786X
eISSN
1996-756X
DOI
10.1117/12.2204778
Publisher site
See Article on Publisher Site

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.

Journal

Proceedings of SPIESPIE

Published: Dec 14, 2015

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