Abstract: A later-fusion multiple kernel clustering machine learning method and system based on proxy graph improvement is provided. The method includes: S1. acquiring a clustering task and a target data sample; S2. initializing a proxy graph improvement matrix; S3. running k-means clustering and graph improvement on each view corresponding to the acquisition of the clustering task and the target data sample, and constructing an objective function by combining kernel k-means clustering and graph improvement methods; S4. cyclically solving the objective function constructed in step S3 so as to obtain a graph matrix, which is fused with basic kernel information; and S5. performing spectral clustering on the obtained graph matrix, so as to obtain a final clustering result. By means of the method, an optimized basic division not only has information of a single kernel, but can also obtain global information by means of a proxy graph.

Abstract: A late fusion multi-view clustering method and system based on local maximum alignment are provided. The late fusion multi-view clustering method based on local maximum alignment includes the following steps: S1: acquiring a clustering task and a target data sample; S2: initializing a permutation matrix of each view and a combination coefficient of each view, and performing average partition of kernel k-means clustering on an average kernel to obtain a neighbor matrix of each view; S3: calculating basic partition of each view, and establishing a late fusion multi-view clustering objective function based on maximum alignment; S4: acquiring basic partition having local information, and establishing a late fusion multi-view clustering objective function based on local maximum alignment; S5: solving the established late fusion multi-view clustering objective function based on local maximum alignment in a cyclic manner to obtain optimal partition; and S6: performing k-means clustering on the optimal partition.