Comprehensive evaluation method, device and application of low-dimensional successive projection pursuit clustering model

Abstract

The invention discloses a comprehensive evaluation method, device and application of a Low Dimensional Successive Projection Pursuit Clustering (LDSPPC) model. A one-dimensional projection pursuit clustering model with 2 to 4 projection vectors orthogonal to each other; the projection pursuit clustering model vectors of all dimensions of multiple candidate objects are synthesized into a comprehensive projection pursuit clustering model, and the evaluation index is important A sorted list for sex and a sorted list for candidate quality. The group search intelligent algorithm of the present invention has the characteristics of fast convergence speed and high reliability of converging to the global optimal solution. Vector synthesis of multiple successive projection tracking vectors can quickly evaluate the quality of candidate objects and improve the accuracy of candidate object quality evaluation. Spend.

Description

technical field [0001] The invention relates to the technical field of computer applications, in particular to a comprehensive evaluation method, device and application based on a low-dimensional successive projection-pursuit clustering model (LDSPPC). Background technique [0002] Supplier selection and evaluation involves data processing with nonlinear and non-normal distribution of multi-indicator (high-dimensional) attributes, and the effect of conventional modeling methods is poor. The one-dimensional Projection Pursuit Clustering (PPC) model proposed by Friedman et al. in 1974 has been widely used in many fields and achieved certain results. However, for the problem of supplier selection and evaluation with multiple attributes and few samples, it is often difficult to select a suitable supplier because the information of the sample data mined is insufficient, and multiple suppliers have the same score. [0003] In the process of LDSPPC modeling, it is first necessary ...

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Problems solved by technology

Gong Yan et al. (2007) used the maximum relative information entropy as the objective function to establish a 9-dimensional projection pursuit clustering model, but there is no actual case data to verify the reliability of the results

These papers did not discuss how to realize the synthesis of multiple projection tracking vectors, which is not conducive to fully mining the sample data information for classification and sorting research

[0006] There is no software found in China that can provide LDSPPC modeling, and the commercialized DPS software developed by Tang Qiyi (2013) cannot obtain reliable results for the PPC modeling program

Since the LDSPPC model is a high-dimensional nonlinear optimization problem with both equality and inequality constraints, it is very difficult to solve

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