Survey of Clustering Based Financial Fraud Detection Research
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[1] Yong Hu,et al. The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature , 2011, Decis. Support Syst..
[2] Miklos A. Vasarhelyi,et al. Application of Anomaly Detection Techniques to Identify Fraudulent Refunds , 2011 .
[3] Rayid Ghani,et al. Interactive learning for efficiently detecting errors in insurance claims , 2011, KDD.
[4] Qingshan Deng,et al. Combining self-organizing map and K-means clustering for detecting fraudulent financial statements , 2009, 2009 IEEE International Conference on Granular Computing.
[5] K. Vanhoof,et al. Data Mining for Fraud Detection: Toward an Improvement on Internal Control Systems? , 2007 .
[6] Anna Jurek,et al. Improving Naïve Bayes models of insurance risk by unsupervised classification , 2008, 2008 International Multiconference on Computer Science and Information Technology.
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[8] Luís Torgo,et al. Utility-Based Fraud Detection , 2011, IJCAI.
[9] Pierre Hansen,et al. Cluster analysis and mathematical programming , 1997, Math. Program..
[10] Koen Vanhoof,et al. Internal Fraud Risk Reduction - Results of a Data Mining Case Study , 2010, ICEIS.
[11] Daniel A. Keim,et al. Visual analytics of large multidimensional data using variable binned scatter plots , 2010, Electronic Imaging.
[12] Rui Liu,et al. Research on anti-money laundering based on core decision tree algorithm , 2011, 2011 Chinese Control and Decision Conference (CCDC).
[13] Girish Keshav Palshikar,et al. Collusion set detection using graph clustering , 2008, Data Mining and Knowledge Discovery.
[14] Wen-Hsi Chang,et al. Using clustering techniques to analyze fraudulent behavior changes in online auctions , 2010, 2010 International Conference on Networking and Information Technology.
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[16] B. Little,et al. Benford’s Law, Data Mining, And FinancialFraud: A Case Study In New York StateMedicaid Data , 2008 .
[17] Jan Schröder,et al. On the Analysis of Irregular Stock Market Trading Behavior , 2007, GfKl.
[18] K. Vanhoof,et al. A Framework for Internal Fraud Risk Reduction at IT Integrating Business Processes, The IFR² Framework , 2009 .
[19] Surya B. Yadav,et al. A computational model for financial reporting fraud detection , 2011, Decis. Support Syst..
[20] Steven K. Donoho,et al. Early detection of insider trading in option markets , 2004, KDD.
[21] M. Tahar Kechadi,et al. Application of Data Mining for Anti-money Laundering Detection: A Case Study , 2010, 2010 IEEE International Conference on Data Mining Workshops.
[22] Jyotindra N. Dharwa,et al. A Data Mining with Hybrid Approach Based Transaction Risk Score Generation Model (TRSGM) for Fraud Detection of Online Financial Transaction , 2011 .
[23] Luís Torgo,et al. Resource-bounded Outlier Detection using Clustering Methods , 2010, Data Mining for Business Applications.
[24] Carolyn F. Holton,et al. Identifying disgruntled employee systems fraud risk through text mining: A simple solution for a multi-billion dollar problem , 2009, Decis. Support Syst..
[25] Sankar Virdhagriswaran,et al. Camouflaged fraud detection in domains with complex relationships , 2006, KDD '06.
[26] Wang Xiaoyun,et al. Hybrid outlier mining algorithm based evaluation of client moral risk in insurance company , 2010, 2010 2nd IEEE International Conference on Information Management and Engineering.
[27] Hui Xiong,et al. COG: local decomposition for rare class analysis , 2010, Data Mining and Knowledge Discovery.
[28] Miklos A. Vasarhelyi,et al. Cluster Analysis for Anomaly Detection in Accounting Data: An Audit Approach 1 , 2011 .
[29] Xindong Wu,et al. Identifying bridging rules between conceptual clusters , 2006, KDD '06.
[30] Philip S. Yu,et al. Applying data mining in investigating money laundering crimes , 2003, KDD '03.