LASCA: A Large-Scale Stable Customer Segmentation Approach to Credit Risk Assessment-Reference-Cited by-同舟云学术

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LASCA: A Large-Scale Stable Customer Segmentation Approach to Credit Risk Assessment

Published:2024-08-24 Issue: Volume: Page:5006-5017
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Gu Yongfeng¹^ORCID,Wu Yupeng²^ORCID,Lu Huakang²^ORCID,Lu Xingyu¹^ORCID,Qian Hong²^ORCID,Zhou Jun¹^ORCID,Zhou Aimin²^ORCID

Affiliation:

1. Ant Group, Hangzhou, Zhejiang, China

2. School of Computer Science and Technology, East China Normal University, Shanghai, China

Funder

Scientific and Technological Innovation 2030 Major Projects

Natural Science Foundation of Shanghai

Ant Group

National Natural Science Foundation of China

CCF-AFSG Research Fund

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671550

Reference43 articles.

1. Segmentation-based modeling for advanced targeted marketing

2. On changing continuous attributes into ordered discrete attributes

3. A Data-Driven Approach for Multi-level Packing Problems in Manufacturing Industry

4. PurTreeClust: A Clustering Algorithm for Customer Segmentation from Massive Customer Transaction Data

5. A fast and elitist multiobjective genetic algorithm: NSGA-II

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