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International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011)

Chen Ming
Chen Ming
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It is difficult to realize on this problem that the spectral clustering algorithm in solving the corresponding eigenvectors of eigenvalues which existing in the drawback of occupation large storage space and high time complexity when it is used to achieve large data sets clustering. For this problem, a double-direction shrink and multi-segmentation and from down to up QR algorithm is proposed in this article, and in which the dimensionality reduction ability of spectral clustering is used to attain the distribution of data in the mapping space. Next, a new quantum genetic spectral clustering algorithm is proposed to cluster the sample points in the mapping space. Compact input with low-dimension for quantum genetic spectral clustering is obtained after mapping, and the quantum genetic spectral clustering algorithm, characterized by its rapid convergence to global optimum and minimal sensitivity to initialization, can obtain good clustering results. In order to confirm the algorithm which this article proposed, the experimental result on UCI data sets and digital image showed that it is more superior clustering results to the spectral clustering algorithm, K-means ,NJW .

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