Gaimei Wang

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Spectral clustering (SC), as an unsupervised learning algorithm, has been used successfully in the field of computer vision for data clustering. In some applications, however, background prior knowledge can be easily obtained, such as pairwise constraints. Therefore, semi-supervised learning is getting increasing attention in recent years. In this paper, a(More)
A new clustering approach namely immune spectral clustering algorithm (ISCA) is proposed in this paper. It combines spectral clustering with immune algorithm for data clustering. In this algorithm, making use of the dimension reduction ability of the spectral clustering algorithm, an immune clonal clustering algorithm is used to cluster the data points in(More)
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