Project Evaluation of Financial Guarantee Based on Improved Spectral Clustering

Author

Weiquan Sang ; Xiaoping Zhang ; Hui Li

Author_Institution

Coll. of Comput. Sci. & Technol., Guizhou Univ., Guiyang, China

Volume

2

fYear

2014

Firstpage

357

Lastpage

361

Abstract

Reasonable and right decisions are the keys to the successful financing guarantee project, and the core of decision-making is the correct evaluation. The improved spectral clustering algorithm is used to build the financing guarantee project evaluation model, which can avoid the set of scale factor, and reduce the computational complexity of matrix eigenvalue decomposition. The financing guarantee project evaluation model is established by MATLAB software, and the effectiveness and high efficiency of CMSC can be verified through the trainings and simulation experiments.

Keywords

computational complexity; decision making; eigenvalues and eigenfunctions; financial data processing; matrix algebra; pattern clustering; project management; CMSC; MATLAB software; computational complexity; decision-making; financial guarantee; financing guarantee project evaluation model; matrix eigenvalue decomposition; spectral clustering; Clustering algorithms; Data models; Indexes; Industries; Mathematical model; Matrix decomposition; Training; Index system; Risk assessment; Spectral clustering; financial guarantee;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Design (ISCID), 2014 Seventh International Symposium on

Print_ISBN

978-1-4799-7004-9

Type

conf

DOI

10.1109/ISCID.2014.224

Filename

7082006