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For the character of traditional clustering algorithms can only deal with low-dimensional linear data, not sensitive to high-dimensional nonlinear data sets, this paper proposes to use normalized cut (N-cut) spectral clustering algorithm to process high-dimensional data, and combines with sparse subspace clustering algorithm to generate similarity matrix, the method overcomes the insensitivity of...
In this paper, our main work is to study and solve the constrained global optimization problem with fractional and trigonal deviation integral and penalty technique. Furthermore we examine the penalty optimality condition and fractional and trigonal penalty deviation integral algorithm for constrained global optimization problem. Combining the Monte-Carlo technique, we analyse specific examples for...
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