教師資料查詢 | 類別: 期刊論文 | 教師: 蔣璿東 Chiang Ding-an (瀏覽個人網頁)

標題:A combined mining-based framework for predicting telecommunications customer payment behaviors
學年102
學期1
出版(發表)日期2013/11/01
作品名稱A combined mining-based framework for predicting telecommunications customer payment behaviors
作品名稱(其他語言)
著者Chen, Chun-Hao; Chiang, Rui-Dong; Wu, Terng-Fang; Chu, Huan-Chen
單位淡江大學資訊工程學系
出版者Kidlington: Pergamon
著錄名稱、卷期、頁數Expert Systems with Applications 40(16), pp.6561-6569
摘要Most existing data mining algorithms apply data-driven data mining technologies. The major disadvantage of this method is that expert analysis is required before the derived information can be used. In this paper, we thus adopt a domain-driven data mining strategy and utilize association rules, clustering, and decision trees to analyze the data from fixed-line users for establishing a late payment prediction system, namely the Combined Mining-based Customer Payment Behavior Predication System (CM-CoP). The CM-CoP could indicate potential users who may not pay the fee on time. In the implementation of the proposed system, first association rules were used to analyze customer payment behavior and the results of analysis were used to generate derivative attributes. Next, the clustering algorithm was used for customer segmentation. The cluster of customers who paid their bills was found and was then deleted to reduce data imbalances. Finally, a decision tree was utilized to predict and analyze the rest of the data using the derivative attributes and the attributes provided by the telecom providers. In the evaluation results, the average accuracy of the CM-CoP model was 78.53% under an average recall of 88.13% and an average gain of 11.2% after a six-month validation. Since the prediction accuracy of the existing method used by telecom providers was 65.60%, the prediction accuracy of the proposed model was 13% greater. In other words, the results indicate that the CM-CoP model is effective, and is better than that of the existing approach used in the telecom providers.
關鍵字Late payment prediction system; Association rules; Clustering; Decision trees; Domain-driven data mining
語言英文
ISSN0957-4174;1873-6793
期刊性質國外
收錄於SCI;
產學合作
通訊作者Chiang, Rui-Dong
審稿制度
國別英國
公開徵稿
出版型式,電子版,紙本
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