Investment Pattern Clustering Based on Online P2P Lending Platform

被引：0

作者：

Shen, Fan ^{[1
]}

Luo, Nianlong ^{[2
]}

机构：

[1] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China

[2] Tsinghua Univ, Ctr Informat Technol, Beijing 100084, Peoples R China

来源：

2016 IEEE/ACIS 15TH INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE (ICIS) | 2016年

关键词：

time series; investment pattern clustering; trajectory; key points; correlation coefficient;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

With the rapid development of online peer-to-peer (P2P) lending platforms in recent years, more and more people participate in the borrowing and lending transactions online. The risk attitude of online lenders normally determines the size of capital invested on the platforms during different periods. Although investment time series from each lender is unique, they share similar characteristics of investment trends. Based on the data from PPDAI platform in China, this paper proposes an effective approach of data preprocessing, namely Key Points Approximate Fitting (KPAF) algorithm, to identify different investment patterns. The KPAF algorithm contributes to a better accuracy of clustering.

引用

页码：181 / 186

页数：6

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