A combined mining-based framework for predicting telecommunications customer payment behaviors

doi:10.1016/j.eswa.2013.06.001

機構典藏 > College of Engineering > Graduate Institute & Department of Computer Science and Information Engineering > Journal Article > Item 987654321/96116

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Title:	A combined mining-based framework for predicting telecommunications customer payment behaviors
Authors:	Chen, Chun-Hao;Chiang, Rui-Dong;Wu, Terng-Fang;Chu, Huan-Chen
Contributors:	淡江大學資訊工程學系
Keywords:	Late payment prediction system;Association rules;Clustering;Decision trees;Domain-driven data mining
Date:	2013-11-01
Issue Date:	2014-02-27 15:08:53 (UTC+8)
Publisher:	Kidlington: Pergamon
Abstract:	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.
Relation:	Expert Systems with Applications 40(16), pp.6561-6569
DOI:	10.1016/j.eswa.2013.06.001
Appears in Collections:	[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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