Lattice clustering and its application in credit risk management of commercial banks

被引：5

作者：

Lin, Jing ^{[1
]}

Han, Lu ^{[1
]}

机构：

[1] Cent Univ Finance & Econ, Sch Management Sci & Engn, Beijing 100081, Peoples R China

来源：

PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE OF INFORMATION AND COMMUNICATION TECHNOLOGY | 2021年 / 183卷

关键词：

Clustering; Lattice; kNN;

D O I：

10.1016/j.procs.2021.02.043

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

One of the classical methods of clustering is kNN and it is a simple and widely used algorithm. But when it comes to the samples with unstructured and fuzzy values, it is not applicable enough course it is based on the Euclidean distance, which does not have practical significant for such data . But these kinds of data are much important in credit risk management of commercial banks, therefore we must do a deal with these important variables by fuzzy division, and calculate the fuzzy distance between every two samples within the lattice degree, then generate a fuzzy division matrix to replace the Euclidean Distance in kNN algorithm. (C) 2021 The Authors. Published by Elsevier B.V.

引用

页码：145 / 151

页数：7

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