Parallel CSA-FCM Clustering Algorithm Based on MapReduce

DOI

10.2991/saeme-17.2017.115How to use a DOI?

Keywords

FCM, CSA, Noise Immunity, MapReduce

Abstract

Fuzzy C-Means (FCM) algorithm is a kind of widely used clustering algorithm, which is widely used in pattern recognition, image processing, medical research and other fields. But FCM doesn't have better performance suppressing noise. A parallel clustering algorithm based on MapReduce is proposed in this paper, which combines Clonal Selection Algorithm and the algorithm uses intelligent optimization method to optimize the initial clustering center, and makes use of the global search ability of CSA to make the algorithm more robust. The algorithm process is designed to conform to the MapReduce programming model and it has the ability of dealing with large-scale dataset. The experiments prove that parallel Clonal Selection Algorithm-Fuzzy C-Means (CSA-FCM) can improves the searching performance and the noise immunity and has high speed up and scalability.

Copyright

© 2017, 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  - Chunchun Cui
AU  - Runtong Zhang
PY  - 2017/07
DA  - 2017/07
TI  - Parallel CSA-FCM Clustering Algorithm Based on MapReduce
BT  - Proceedings of the 2017 International Conference on Sports, Arts, Education and Management Engineering (SAEME 2017)
PB  - Atlantis Press
SP  - 512
EP  - 516
SN  - 2352-5398
UR  - https://doi.org/10.2991/saeme-17.2017.115
DO  - 10.2991/saeme-17.2017.115
ID  - Cui2017/07
ER  -