| 基于人工免疫聚类的欺诈客户分析 |
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| 引用本文： | 脱建勇,李秀,任守榘,刘文煌.基于人工免疫聚类的欺诈客户分析[J].清华大学学报(自然科学版),2011(7). |
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| 作者姓名： | 脱建勇 李秀 任守榘 刘文煌 |
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| 作者单位： | 清华大学自动化系;北京化工大学信息学院; |
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| 基金项目： | 国家自然科学基金项目(70671059) |
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| 摘 要： | 针对传统aiNet聚类算法在处理欺诈客户分析时聚类精度差的问题,该文基于一种新的免疫聚类算法(SE-aiNet)提出了利用子群群体特征进化的免疫聚类算法,使收敛速度有显著提高,并在保持抗体种群规模的情况下有效提高了抗体的多样性;以训练所得的规则集来判断客户是否欺诈,经某银行的实际数据验证,算法的误分比例为24.0%,而相同环境下BP-NN算法的误分比例为32.3%,C-SVM的误分比例为28.6%。
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| 关 键 词： | 聚类算法 欺诈客户识别 免疫聚类 客户关系管理 |
Fraudulent customer analysis based on artificial immune clustering |
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| TUO Jianyong,LI Xiu,REN Shouju,LIU Wenhuang.Fraudulent customer analysis based on artificial immune clustering[J].Journal of Tsinghua University(Science and Technology),2011(7). |
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| Authors: | TUO Jianyong LI Xiu REN Shouju LIU Wenhuang |
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| Institution: | TUO Jianyong1,2,LI Xiu1,REN Shouju1,LIU Wenhuang1(1.Department of Automation,Tsinghua University,Beijing 100084,China,2.School of Information,Peking University of Chemistry,Beijing 100029,China) |
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| Abstract: | The traditional artificial immune clustering algorithm,aiNet,has a slow convergence rate.A new immune clustering algorithm,SE-aiNet,was developed by adding a sub-net evolution operator to aiNet.The sub-net evolution clustering algorithm improves the diversity of the antibodies and the similarities inside the same sub-set.The algorithm improves the clustering speed,while the antibodies help to distinguish fraudulent customer from real users.Tests on a bank data show that the SE-aiNet algorithm has a normaliz... |
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| Keywords: | clustering algorithm fraud detection immune algorithm customer relation management |
| 本文献已被 CNKI 等数据库收录！ |
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