Multi-channel spectrum clustering method based on local density estimation and neighbor relation spreading

Abstract

The invention discloses a multi-channel spectral clustering method based on local density estimation and neighbor relation spreading. The multi-channel spectrum clustering method based on local density estimation and neighbor relation spreading mainly solves the problem that an existing clustering method cannot carry out clustering on data distributed unevenly in density. The multi-channel spectrum clustering method based on local density estimation and neighbor relation spreading comprises the steps that local density of a sample is estimated and is used as data characteristics and dimension lifting is carried out on original data; a distance matrix, a threshold value and a similarity matrix are calculated, and a neighbor relation matrix is initialized; the neighbor relation matrix and the similarity matrix are updated, and similarity of samples of a subset is updated by the adoption of a local maximum similar value, and an accurate affinity matrix is obtained; a similarity matrix and a normalized Laplacian matrix are calculated; a spectrum matrix is normalized, and a clustering result is obtained through the K-means algorithm. Compared with an existing clustering technology, the multi-channel spectrum method based on local density estimation and neighbor relation spreading enables a more real similarity matrix to be obtained, the clustering result is more accurate and the robustness is better.

Description

technical field [0001] The invention belongs to the technical field of cluster analysis and relates to a method for constructing an improved affinity matrix in spectral clustering. Specifically, it is a multi-way spectral clustering method based on local density estimation and neighbor relationship propagation, which can be used in systems such as data mining, image segmentation and machine learning. Background technique [0002] The spectral clustering technology is based on the spectral graph theory, and its essence is to use the method of spectral relaxation to transform the clustering problem into the optimal partition problem of the graph. Firstly, according to the given data set, an affinity matrix is ​​defined to describe the similarity between data points, and the eigenvalues ​​and eigenvectors of the standardized affinity matrix are calculated, and different data points are clustered by selecting appropriate eigenvectors. Compared with traditional clustering algori...

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Problems solved by technology

Although this method can amplify the similarity of samples in the same subset, the similarity of samples between subsets is measured by the global minimum value of the similarity matrix W, which reduces the similarity between different subsets belonging to the same class. And for data with uneven density distribution, it is easy to divide different types of samples into subsets, resulting in the constructed affinity matrix not being able to truly reflect the similarity between samples, and the clustering results are inaccurate

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