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A big data spectrum clustering algorithm based on weighted set Nystr* m sampling

A spectral clustering algorithm and big data technology, applied in the field of big data spectral clustering algorithms based on weighted set sampling, can solve the problems of low practicability of spectral clustering, large space-time overhead, etc.

Pending Publication Date: 2019-06-25
LIAONING TECHNICAL UNIVERSITY
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Problems solved by technology

However, because spectral clustering needs to store the entire similarity matrix and perform eigendecomposition during the execution process, it needs to consume a lot of time and space overhead, which makes spectral clustering very low in practicality in large-scale data.

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  • A big data spectrum clustering algorithm based on weighted set Nystr* m sampling
  • A big data spectrum clustering algorithm based on weighted set Nystr* m sampling
  • A big data spectrum clustering algorithm based on weighted set Nystr* m sampling

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Embodiment Construction

[0053] The application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain related inventions, rather than to limit the invention. It should also be noted that, for ease of description, only parts related to the invention are shown in the drawings.

[0054] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and embodiments.

[0055] In order to test the effectiveness of the WES-SC algorithm, five clustering algorithms are selected in the experiment to compare with the algorithm in this paper, and the performance of the algorithm is tested on the benchmark data set. The spectral clustering algorithm based on approximate...

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Abstract

The invention discloses a big data spectrum clustering algorithm based on weighted set sampling. The method is characterized by comprising a set method and a weighted set, the set method is characterized in that each approximation generated by m columns of samples through a standard method is regarded as an expert, p is larger than or equal to one expert, and in the weighted set, an integral variable in an integral equation obtained through the set method is changed into a weight function. The matrix low-rank approximation method can solve the approximated feature vector and feature space, andis a powerful tool for processing big data. In spectral clustering, an expansion technology is used for approximate calculation, time and space expenditure can be reduced to a great extent, and the efficiency of an algorithm is greatly improved with small precision loss.

Description

technical field [0001] The present disclosure generally relates to the field of computer learning, and specifically relates to a weighted set based A sampled spectral clustering algorithm for big data. Background technique [0002] Spectral clustering is a clustering method based on graph theory. Spectral graph theory transforms the data clustering problem into a graph multi-way partition problem, and clusters data points by dividing subgraphs. Given a data set containing N points, an N×N similarity matrix can be constructed according to the pairwise similarity of data points. The spectral method is based on the eigenvectors and eigenvalues ​​of the similarity matrix to cluster. Using these eigenvectors, a low-dimensional embedding subspace of data points can be constructed, in which data points can be clustered using simple central clustering methods such as k-means. Therefore, cluster analysis is one of the important research contents in the field of data mining, and i...

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Application Information

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IPC IPC(8): G06K9/62
Inventor 邱云飞刘畅
Owner LIAONING TECHNICAL UNIVERSITY
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