An Effective Fraud Detection Method Based on Clustering

• Main Authors: Kai-Hsun Chung, 詹凱薰
• Other Authors: Jou-Shien Chang
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/598at9

碩士 === 淡江大學 === 資訊管理學系碩士班 === 104 === Online auction attracts a lot of speculators using dishonest tricks to obtain illegal benefits. This causes consumers’ loss, including time and money, and have negative impact on development of e-commerce in the future. For this reason, researchers have proposed a variety of fraud detection method to help users to avoid fraud. However, faced with the evolving fraud techniques, existing methods cannot provide satisfied detection accuracy for consumers. In view of this, this study developments a new dynamic fraud detection method. First, we collect the information from web pages of the Yahoo!Taiwan auction site and filter them with removing outliers. Second, we cluster those data into frauds and non-frauds categories. Finally, finding the best cluster combination of frauds and non-frauds sub-models according to detecting result of test data. To verify the effectiveness of this proposed method, the transaction data in Yahoo! Taiwan are gathered for experiments. In comparison with the single decision tree, the proposed dynamic detection method do help to improve the detection, and have stable detection results for different data set.