Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Data Mining and Knowledge Discovery
Machine Learning
Predicting Customer Behavior in Telecommunications
IEEE Intelligent Systems
Intrusion detection using fuzzy association rules
Applied Soft Computing
Domain-Driven Data Mining: Challenges and Prospects
IEEE Transactions on Knowledge and Data Engineering
Bridging Domains Using World Wide Knowledge for Transfer Learning
IEEE Transactions on Knowledge and Data Engineering
Knowledge-Based Interactive Postmining of Association Rules Using Ontologies
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Asking Generalized Queries to Domain Experts to Improve Learning
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Signaling Potential Adverse Drug Reactions from Administrative Health Databases
IEEE Transactions on Knowledge and Data Engineering
Flexible Frameworks for Actionable Knowledge Discovery
IEEE Transactions on Knowledge and Data Engineering
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting
Knowledge-Based Systems
Using ontologies to facilitate post-processing of association rules by domain experts
Information Sciences: an International Journal
Mining fuzzy association rules in a bank-account database
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
Smart meter monitoring and data mining techniques for predicting refrigeration system performance
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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.