Research and Simulation on effective classification model of nonlinear data

DOI

10.2991/amcce-15.2015.314How to use a DOI?

Keywords

nonlinear data; classification; clustering;

Abstract

in the classification process of nonlinear data, due to large amount of the data of nonlinearity, the correlation between nonlinear data is reduced, resulting in the classification results of nonlinear data is not ideal. Therefore, this paper proposes a fast clustering algorithm based on improved quantum genetic evolutionary incentive, the algorithm is used to classify nonlinear data effectively. In this algorithm, firstly, high density partition and threshold parameters are utilized to process first cluster partition on nonlinear data sets, a number of clustering are generated; and then the clustering process of samples is regarded as dynamic optimization process of cluster centers, improved quantum genetic algorithm is adopted to search optimal clustering center of each cluster; adaptive mutation operator is introduced to improve the search ability of evolutionary algorithm, so as to enhance the global search capability of the algorithm. The experimental results show that, with the improved algorithm to conduct nonlinear data classification optimization processing, can improve the accuracy of classification, and achieve satisfactory results.

Copyright

© 2015, 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  - Fenglin Tang
AU  - Min Chen
PY  - 2015/04
DA  - 2015/04
TI  - Research and Simulation on effective classification model of nonlinear data
BT  - Proceedings of the 2015 International Conference on Automation, Mechanical Control and Computational Engineering
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/amcce-15.2015.314
DO  - 10.2991/amcce-15.2015.314
ID  - Tang2015/04
ER  -