Spectral clustering method based on Kendall Tau distance fused measurement

Abstract

The invention provides a spectral clustering method based on Kendall Tau distance fused measurement, and can be widely applied in a clustering analysis problem. In an existing spectral clustering algorithm, a traditional Euclidean distance is generally used for measuring a similarity between samples, but useful neighborhood information between the samples is neglected. Therefore, the invention provides a new spectral clustering method based on the Kendall Tau distance fused measurement. Firstly, the invention provides a new distance measurement method, the method fuses the Euclidean distance with the Kendall Tau distance in a nonlinear way, and a goal that underlying structure information between the samples is comprehensively mined from multiple perspectives to obtain a strengthened similarity measurement result between the samples. Then, the new similarity measurement result is applied to the spectral clustering algorithm to carry out clustering analysis. By use of the method provided by the invention, structural information between the samples can be comprehensively reflected, and the clustering accuracy of the spectral clustering algorithm is improved.

Description

1. Technical field [0001] The present invention relates to a spectral clustering method based on the fusion of Kendall Tau distance measures, involving distance measures, similarity fusion, spectral clustering analysis, etc. The clustering accuracy of the algorithm is mostly used in the field of data mining. 2. Background technology [0002] With the rapid development of the information age, big data and its related data analysis have received more and more attention. In order to dig out useful information from massive data, one of the most commonly used techniques in data analysis and exploration---cluster analysis has once again become the focus of discussions in various circles. At present, cluster analysis has been successfully applied in fields such as business intelligence, image processing, web search, biology and security. In recent years, spectral clustering has become one of the most popular clustering algorithms and a new research hotspot in the field of machine...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problem that Euclidean distance is commonly used in traditional spectral clustering algorithms, generally only the distance information between these two samples is considered and the information of other surrounding samples is ignored. This invention proposes a new method based on fusion Kendall Tau distance Spectral clustering method, the specific steps are as follows:

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