ACO-based Projection Pursuit clustering algorithm

Authors:
Yancang Li;Lina Zhao;Shujing Zhou
Affiliations:
College of Civil Engineering, Hebei University of Engineering, Handan, China;College of Civil Engineering, Hebei University of Engineering, Handan, China;College of Civil Engineering, Hebei University of Engineering, Handan, China
Venue:
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
Year:
2010

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Abstract

In order to find a more effective method of solving the problem of subjectivity and difficulty to deal with the high-dimension data in the clustering, a new method--an improved PP (Projection Pursuit) based on Ant Colony Optimization algorithm (ACO) was introduced. The ant colony optimization algorithm has the strong global optimization ability and the PP method is a powerful technique for extracting statistically significant features from high-dimension data for automatic target detection and classification. The ant colony optimization algorithm was employed to optimize the function of the projected indexes in the PP. Application results show that the method can complete the selection more objectivity and rationality with objective weight, high resolving power, and stable result.