RFCM clustering algorithm based on adaptive weights for radar signal sorting

DOI

10.2991/mmme-16.2016.28How to use a DOI?

Keywords

clustering; adaptive weights; equivalence factor; radar signal sorting

Abstract

Rough Fuzzy C-Means (RFCM) clustering algorithm is a valid algorithm to process the inseparability of bor-der of clusters, which handles isolated data well and reduces influence of noise on clusters results. However, different data objects are unified with fixed weights, which influences clusters results greatly. In order to solve the problem, RFCM clustering algorithm based on self-adaptive weights is proposed. According to dif-ferent distance between every data object and clustering center, using improved arc cotangent function to re-distribute the distance, acquiring adaptive weights through the equivalence factor in each iteration, then carry-ing out RFCM clustering algorithm. The simulations are performed on UCI data sets, and the results show the validity of the proposed algorithm. Furthermore, the proposed algorithm is applied in sorting radar signal, and the results show the practicability of the proposed algorithm.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qiang Zhang
AU  - Hongwei Wang
AU  - Yuanzhi Yang
AU  - Wenzhe Wang
PY  - 2016/10
DA  - 2016/10
TI  - RFCM clustering algorithm based on adaptive weights for radar signal sorting
BT  - Proceedings of the 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering
PB  - Atlantis Press
SP  - 127
EP  - 130
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmme-16.2016.28
DO  - 10.2991/mmme-16.2016.28
ID  - Zhang2016/10
ER  -