High-dimensional data clustering method, device and equipment, medium and product

Abstract

The invention discloses a high-dimensional data clustering method and device, equipment, a medium and a product, and relates to the field of clustering analysis in machine learning, and the method comprises the steps: updating a membership matrix according to a cluster center matrix, a projection matrix and a cluster pair similarity matrix; the membership matrix is constructed based on a local information factor of a high-dimensional data sample; updating the cluster center matrix; according to the original high-dimensional data matrix, the updated membership matrix, the updated cluster center matrix and the cluster pair similarity matrix, an auxiliary matrix is constructed, eigenvalue decomposition is solved, and a projection matrix is updated; constructing a convex optimization problem according to the updated membership matrix, the updated cluster center matrix and the updated projection matrix, and updating a cluster pair similarity matrix; judging whether a termination condition is met or not; if yes, dividing the high-dimensional data sample according to the updated membership matrix, and outputting a clustering result; and if not, returning to the membership degree matrix updating step. According to the method, the clustering result of the clustering method on the high-dimensional data can be improved.

Images