Projection pursuit dynamic cluster method for multidimensional index based on particle swarm optimization

A particle swarm algorithm and projection pursuit technology, applied in the field of clustering evaluation of high-dimensional index data, can solve problems such as programming difficulties, complex calculations, and difficulty in popularization and use, so as to avoid excessive subjectivity and ordinary projection pursuit The effect of ignoring the weight of indicators and good practical value

Inactive Publication Date: 2013-08-07
ZHEJIANG GONGSHANG UNIVERSITY
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Problems solved by technology

In terms of technology to realize projection pursuit, the multiple smoothing regression technology proposed by Friedman is complex in calculation, difficult in programming, and difficult to popularize and use.

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  • Projection pursuit dynamic cluster method for multidimensional index based on particle swarm optimization
  • Projection pursuit dynamic cluster method for multidimensional index based on particle swarm optimization
  • Projection pursuit dynamic cluster method for multidimensional index based on particle swarm optimization

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Embodiment Construction

[0030] The present invention will be further described below in conjunction with specific embodiments.

[0031] The projection pursuit dynamic clustering method based on the multi-dimensional index of the particle swarm optimization algorithm proposed by the present invention comprises the following steps:

[0032] Step 1: Build a multi-level evaluation index system

[0033] (1) Multi-level evaluation index system, the multi-level index system is the preliminary work of the application of the present invention. Different multi-level and multi-dimensional evaluation index systems need to be constructed for different specific fields and different goals.

[0034] (2) AHP method assigns preliminary weights, and uses AHP method to preliminarily determine index weights. The importance of each evaluation index to the target layer is different. On the basis of determining the multi-level evaluation index system, through the mathematical method of constructing a pairwise comparison ...

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Abstract

The invention discloses a projection pursuit dynamic cluster method for a multidimensional index based on particle swarm optimization, which effectively combines the advantages of APH, the projection pursuit, the particle swarm optimization and the like, and provides a new projection pursuit dynamic cluster method for the multidimensional index based on the particle swarm optimization. The new method effectively avoids the problems that the subjectivity of the AHP is too strong and the ordinary projection pursuit neglects index weight. Through the application of the method, cluster evaluations of high-capacity and high-dimensionality multidimensional index data of business can be realized, therefore, the method has high practical value.

Description

technical field [0001] The invention relates to the technical field of high-dimensional data clustering, in particular to a multi-dimensional index projection pursuit dynamic clustering method based on a particle swarm algorithm, and is particularly suitable for clustering evaluation of high-dimensional index data for processing large-capacity businesses. Background technique [0002] The traditional "assumption-simulation-prediction" confirmatory data analysis statistical model is difficult to adapt to the ever-changing objective world and real-time changing big data. When it is used for high-dimensional, nonlinear, and non-normally distributed data evaluation modeling, It didn't work out well. Commercial data usually has the characteristics of large volume and multi-dimensional indicators, and at the same time, the factors affecting data changes are too complex. If the method of statistical assumptions is simply used, it is not only difficult to effectively scientifically...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 鲍福光王宗格刘中军付娉
Owner ZHEJIANG GONGSHANG UNIVERSITY
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