Abstract
In order to find an effective method of solving the problem of subjectivity and difficulty in the high-dimension data clustering, a new method—an improved Projection Pursuit based on Ant Colony Optimization algorithm was introduced. The ant colony optimization algorithm was employed to optimize the function of the projected indexes in the PP. 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. Application results show that the method can complete the selection more objectivity and rationality with objective weight, high resolving power, and stable result. The study provides a novel algorithm for the high-dimension data clustering.
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Li, Y., Zhao, L., Zhou, S. (2010). ACO-Based Projection Pursuit: A Novel Clustering Algorithm. In: Zaman, M., Liang, Y., Siddiqui, S.M., Wang, T., Liu, V., Lu, C. (eds) E-business Technology and Strategy. CETS 2010. Communications in Computer and Information Science, vol 113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16397-5_9
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DOI: https://doi.org/10.1007/978-3-642-16397-5_9
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