High-dimensional data subspace clustering projection effect optimization method based on dimension reconstitution

Abstract

The present invention provides a high-dimensional data subspace clustering projection effect optimization method based on dimension reconstitution. The high-dimensional data subspace clustering projection effect optimization method based on dimension reconstitution comprises the specific steps of: 1, searching a dimension subspace, i.e. confirming a target optimization subspace of which a two-dimensional projection effect needs to be improved and selecting a subspace with an excellent clustering structure; 2, constructing a reconstructed dimension set, i.e. transferring clustering information of the subspace with the excellent clustering structure to a reconstructed dimension; 3, constructing candidate optimal dimension subspace sets, i.e. carrying out free combination on each element of the reconstructed dimension set and the target optimization dimension subspace to generate the candidate optimal subspace sets; 4, screening an optimal dimension subspace set; 5, determining an optimal dimension subspace. According to the high-dimensional data subspace clustering projection effect optimization method based on dimension reconstitution, the reconstruction concept is creatively introduced into the high-dimensional data subspace, the clustering projection effect of the target optimization subspace is improved and enhanced by the reconstructed dimension with stronger clustering information, and the problem of distortion of the clustering projection effect of the high-dimensional data subspace on the two-dimensional plane is solved.

Description

technical field [0001] The invention relates to the technical field of high-dimensional data analysis, processing and visualization, and specifically uses related concepts and technologies such as subspace clustering, LDA, MDS, and Dunn index to optimize the projection effect of subspace clustering. Background technique [0002] With the rapid development of computer technology in all walks of life, various data are increasing day by day, and a large amount of data in these data is multi-dimensional data or even high-dimensional data. In view of the limitations of human cognitive ability and the lack of imagination of high-dimensional data space, it is still difficult for human beings to obtain the deep information contained in complex high-dimensional data. One of the best result states that clustering methods can achieve is that the similarity between objects belonging to the same cluster is as high as possible, while the similarity between objects belonging to different c...

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Problems solved by technology

In view of the unsatisfactory projection effect of high-dimensional data subs...

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