Projection Pursuit Clustering Algorithms Based On Immune Clonal And Its Applications

With the drastic development of Internet and communication, the accumulation of data is increased both in scale and dimension, which lead to the efficiency of some traditional clustering algorithms reduced. In this thesis, the dimensional reduction method of projection pursuit is researched, which is combined with immune clonal method to construct some new algorithms. Then these algorithms are applied in texture image and SAR image segmentation. The main achievements are as follows:(1)The algorithm of projection pursuit clustering based on immune clonal selection algorithm is proposed. Immune clonal selection algorithm is utilized to optimize the projection index, and then the optimum direction and subspace are obtained. Finally, the better clustering results are acquired in this subspace;(2) The algorithm of immune clonal selection projection pursuit clustering based on LDA index is proposed. In projection pursuit clustering algorithm model, we adopt Immune clonal selection algorithm to optimize the projection index which is the relationship between with-in scatters and between-in scatters, then the optimum direction and subspace are obtained. Meanwhile, the projection pursuit method and K-means clustering are iterated adaptively, and then the better results are obtained;(3) Based on the relationship of labeled and unlabeled data which is constructed by graph Laplacian, the algorithm of semi-supervised projection pursuit clustering is proposed. And the algorithm is applied in texture and SAR image segmentation. Seen from the results, the precision are improved. Moreover, transfer learning is introduced to the algorithm of immune clonal selection projection pursuit clustering based on LDA index. Firstly, transferring the band of the hyperspectral remote images, then do image segmentation. The result of segmentation shows higher precision.