Improved spectral clustering and parallelization method

A spectral clustering and clustering technology, applied in the field of unsupervised learning, which can solve problems such as intractable data

Inactive Publication Date: 2018-09-11
GUILIN UNIV OF ELECTRONIC TECH +1
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Problems solved by technology

With the development of information technology, the diversity of data has been brought, which makes the dimensions of many data irrelevant. It is difficult for traditional clustering algorithms to deal with these unrelated data.

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  • Improved spectral clustering and parallelization method
  • Improved spectral clustering and parallelization method
  • Improved spectral clustering and parallelization method

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Embodiment Construction

[0014] Selecting the eigenvectors corresponding to the first 2k largest eigenvalues ​​of the Laplacian matrix as the source data for clustering can not only produce results that are higher than the clustering accuracy of the ordinary NJW algorithm, but also produce results that are lower than the clustering accuracy of the ordinary NJW algorithm. The result of high rate, the volatility of its multiple clustering results is higher than that of ordinary NJW algorithm clustering results. The volatility of multiple clustering accuracy is due to the feature sample space in spectral clustering, and the low-dimensional space samples are more compact. After expanding from k-dimensional to 2k-dimensional, the introduction of a dimension with poor clustering ability increases the The irrelevance between the data makes the data points more dispersed compared with the low-dimensional space; and because the spectral clustering algorithm also uses random initialization center points, it incr...

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Abstract

The present invention discloses an improved spectral clustering method based on a swarm intelligence algorithm. Feature vectors corresponding to former 2k maximum feature values of a Laplacian matrixare selected as source data of a cluster, and the swarm intelligence algorithm is employed to select high-quality initialization center points for cluster operation so as to improve the maximum accuracy of the cluster and the stability of multi-times cluster results. The Cuckoo search algorithm is introduced to search initialization center points, a fitness function in the process of the Cuckoo search algorithm employs an error square sum function and is applied into the spectral clustering to take data points of the minimum error sum square obtained through search as the initialization centerpoints. The Levy flight strategy in the Cuckoo search algorithm is introduced into the particle swarm optimization algorithm, in a condition that the convergence rate of the particle swarm optimization algorithm is slow, the Levy flight strategy is employed to generate step lengths with small frequencies and occasionally large step lengths, and speed updating formulas for different aspects are introduced in different step lengths.

Description

technical field [0001] The invention belongs to a non-supervised learning method in machine learning, and relates to a clustering method and a group intelligence algorithm. Background technique [0002] The purpose of clustering is to divide the data, divide similar data into the same cluster, and divide dissimilar data into different clusters. With the development of information technology, the diversity of data has been brought, which makes the dimensions of many data irrelevant. It is difficult for traditional clustering algorithms to deal with these uncorrelated data. Spectral clustering is a new type of clustering algorithm, which can cluster in sample space of arbitrary shape and can converge to the global optimal solution, and has been widely used in computer vision, text mining and biological information mining and other fields. At present, the research on spectral clustering mainly includes the construction of similarity matrix, the selection of eigenvectors, the d...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/00
CPCG06N3/006G06F18/2323G06F18/23213
Inventor 强保华孙颢宁王玉峰谢武韦二龙史喜娜赵兴朝
Owner GUILIN UNIV OF ELECTRONIC TECH
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