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Mathematical Problems in Engineering
Volume 2015, Article ID 839081, 14 pages
http://dx.doi.org/10.1155/2015/839081
Research Article

An Associate Rules Mining Algorithm Based on Artificial Immune Network for SAR Image Segmentation

1School of Mathematics and Statistics, Xidian University, China
2School of Science, Xi’an University of Science and Technology, China

Received 15 January 2015; Revised 26 May 2015; Accepted 28 May 2015

Academic Editor: Hak-Keung Lam

Copyright © 2015 Mengling Zhao and Hongwei Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

As a computational intelligence method, artificial immune network (AIN) algorithm has been widely applied to pattern recognition and data classification. In the existing artificial immune network algorithms, the calculating affinity for classifying is based on calculating a certain distance, which may lead to some unsatisfactory results in dealing with data with nominal attributes. To overcome the shortcoming, the association rules are introduced into AIN algorithm, and we propose a new classification algorithm an associate rules mining algorithm based on artificial immune network (ARM-AIN). The new method uses the association rules to represent immune cells and mine the best association rules rather than searching optimal clustering centers. The proposed algorithm has been extensively compared with artificial immune network classification (AINC) algorithm, artificial immune network classification algorithm based on self-adaptive PSO (SPSO-AINC), and PSO-AINC over several large-scale data sets, target recognition of remote sensing image, and segmentation of three different SAR images. The result of experiment indicates the superiority of ARM-AIN in classification accuracy and running time.