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Home > Archive>Volume 31, Issue 3, 2020 >710-725. DOI:10.13328/j.cnki.jos.005905
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Sale Fraud Behavior Detection over Multidimensional Sparse Data Warehouse
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National Natural Science Foundation of China (61772091, 61802035, 61962006); Sichuan Science and Technology Program (20YYJC2785, 2018JY0448, 2019YFG0106, 2019YFS0067); Innovative Research Team Construction Plan in Universities of Sichuan Province (18TD0027); National Natural Science Foundation of Guangxi of China (2018GXNSFDA138005); Scientific Research Foundation for Advanced Talents of Chengdu University of Information Technology (KYTZ201715, KYTZ201750); Scientific Research Foundation for Young Academic Leaders of Chengdu University of Information Technology (J201701); Guangdong Province Key Laboratory of Popular High Performance Computers (2017B030314073)

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    Abstract:

    In distribution channel system, product manufacturer will often reward retail trader who makes big deal to increase the sales. On the other hand, in order to obtain high reward, retail traders may form alliance, where a cheating retail trader accumulates the deals of other retail traders. This type of commercial fraud is called deal cheating or cross region sale. Because the sales contain a lot of normal big deals, traditional outlier detection methods cannot distinguish the normal extreme value and the true outlier generated by deal cheating behavior. Meanwhile, the sparsity of the multidimensional sales data makes the outlier detection methods based on multidimensional space cannot work effectively. To handle the aforementioned problems, this study proposes deal cheating mining algorithms based on ratio characteristic and tensor reconstruction method. These algorithms combine artificial intelligence and database technique. Meanwhile, because there are multiple types of deal cheating patterns, this study proposes deal cheating pattern classification methods based on the partially ordered lattice of deal cheating patterns. In the experiments on synthetic data, the deal cheating detection algorithm based on the ratio characteristic can achieve an average AUC-value of 65%. The traditional feature extraction methods can only achieve average AUC-values of 36% and 30%. In the experiments on the real data, the results shows the deal cheating detection algorithm is capable of distinguishing normal big deal from abnormal big deal which may be generated by the deal cheating behaviors.

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郑皎凌,乔少杰,舒红平,应广华,Louis Alberto GUTIERREZ.面向多维稀疏数据仓库的欺诈销售行为挖掘.软件学报,2020,31(3):710-725

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History
  • Received:July 20,2019
  • Revised:September 10,2019
  • Online: January 10,2020
  • Published: March 06,2020
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