Optimal neighbor multi-kernel clustering method and system based on local kernel

Abstract

The invention discloses an optimal neighbor multi-kernel clustering method and system based on a local kernel, and the method comprises the steps: S11, obtaining a clustering task and a target data sample; S12, calculating a kernel matrix of each view corresponding to the target data sample, and carrying out centralization and normalization processing on the kernel matrix to obtain a processed kernel matrix; s13, according to the obtained processed kernel matrix, establishing an optimal neighbor multi-kernel clustering objective function based on a local kernel; s14, solving the established objective function by adopting a circulation mode to obtain a division matrix after view fusion; and S15, performing k-means clustering on the obtained division matrix to obtain a clustering result.

Description

technical field [0001] The invention relates to the technical field of data analysis, in particular to an optimal neighbor multi-kernel clustering method and system based on local kernels. Background technique [0002] Kernel clustering has been extensively explored in the current machine learning and data mining literature. It implicitly maps the original inseparable data to a high-dimensional Hilbert space, where the corresponding vertices have well-defined decision boundaries. Then, various clustering methods are applied, including k-means [K.Krishna and N.M.Murty, “Genetic k-means algorithm,” IEEE Transactions on Systems Man And Cybernetics-Part B: Cybernetics, vol.29, no.3, pp. 433–439, 1999], Fuzzy c-means [J.C. Bezdek, R. Ehrlich, and W. Full, “Fcm: The fuzzy c-means clustering algorithm,” Computers & Geosciences, vol.10, no.2-3, pp.191– 203, 1984], spectral clustering [A.Y.Ng, M.I.Jordan, and Y.Weiss, “Onspectral clustering: Analysis and an algorithm,” in Advances ...

AI Technical Summary

Problems solved by technology

[0005] The above MKC algorithm has two problems: it does not fully consider the local density around a single data sample and excessively restricts the representation ability of learning the optimal kernel

Therefore, such local kernels cannot minimize unreliability due to ignoring the local density around a single data sample

At the same time, most multi-kernel clustering algorithms assume that the optimal kernel is a weighted combination of pre-specified kernels, while ignoring some more robust kernels in the complement of kernel combinations

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