Using data mining to integrate Recency-Frequency-Monetary value (RFM) analysis and credit scoring methods for bank customer behavior analysis

IF 0.4 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Data Mining Modelling and Management Pub Date : 2023-01-01 DOI:10.1504/ijdmmm.2023.10055838
Pantea Parsi, Mohammad Khanbabaei, Najmeh Farhadi
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利用数据挖掘技术,将RFM分析方法与信用评分方法相结合,用于银行客户行为分析
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来源期刊
International Journal of Data Mining Modelling and Management
International Journal of Data Mining Modelling and Management COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
1.10
自引率
0.00%
发文量
22
期刊介绍: Facilitating transformation from data to information to knowledge is paramount for organisations. Companies are flooded with data and conflicting information, but with limited real usable knowledge. However, rarely should a process be looked at from limited angles or in parts. Isolated islands of data mining, modelling and management (DMMM) should be connected. IJDMMM highlightes integration of DMMM, statistics/machine learning/databases, each element of data chain management, types of information, algorithms in software; from data pre-processing to post-processing; between theory and applications. Topics covered include: -Artificial intelligence- Biomedical science- Business analytics/intelligence, process modelling- Computer science, database management systems- Data management, mining, modelling, warehousing- Engineering- Environmental science, environment (ecoinformatics)- Information systems/technology, telecommunications/networking- Management science, operations research, mathematics/statistics- Social sciences- Business/economics, (computational) finance- Healthcare, medicine, pharmaceuticals- (Computational) chemistry, biology (bioinformatics)- Sustainable mobility systems, intelligent transportation systems- National security
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