Data processing method based on subspace clustering

Abstract

The invention discloses a data processing method based on subspace clustering, comprising steps of extracting characteristic points from all data which need to perform subspace clustering, performing normalization process on all extracted characteristic points to obtain a characteristic point matrix, establishing an adjacent set Omega for every characteristic point x which has gone through the normalization processing, constructing a similarity matrix W between all characteristic points according to the adjacent set of every characteristic point x, calculating a Laplacian matrix L corresponding to the similarity matrix W among all characteristic points, performing spectrum clustering segmentation on each Laplacian matrix L to obtain a category label of every characteristic points, and realizing the subspace clustering process of all the data. The data processing method based on the subspace clustering can effectively perform clustering process on the big scale data while guaranteeing high accuracy, satisfies the need for processing data in big scale, and is applicable to the data processing fields like the image processing, computer vision and image movement segmentation.

Description

technical field [0001] The invention relates to the technical fields of data processing such as pattern recognition and digital image processing, and in particular to a data processing method based on subspace clustering. Background technique [0002] At present, cluster analysis is one of the key technologies in the field of data mining. High-dimensional data clustering is the difficulty and focus of cluster analysis technology. Subspace clustering is an effective way to realize high-dimensional data set clustering. It is an extension of traditional clustering algorithms in high-dimensional data space. Its The idea is to localize the search in relevant dimensions. Because of its applicability in real life, subspace clustering technology has a wide range of applications in image segmentation, motion segmentation, face clustering, image compression and representation, etc. The purpose of subspace clustering is to find clusters in a mixed high-dimensional space. The basic as...

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Problems solved by technology

Therefore, among the existing data processing methods based on subspace clustering based on spectral clustering, the method based on optimization has high time complexity, while the clustering result of the method based on greedy algorithm is easily affected by noise points. Not strong, so the existing subspace clustering data processing methods are not suitable for large-scale data processing

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