Mining the Banking Customer Behavior Using Clustering and Association Rules Methods
سال انتشار: 1389
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 677
فایل این مقاله در 7 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_IJIEPR-21-4_007
تاریخ نمایه سازی: 7 شهریور 1393
چکیده مقاله:
The unprecedented growth of competition in the banking technology has raised the importance of retaining current customers and acquires new customers so that is important analyzing Customer behavior, which is base on bank databases. Analyzing bank databases for analyzing customer behavior is difficult since bank databases are multi-dimensional, comprised of monthly account records and daily transaction records. Few works have focused on analyzing of bankdatabases from the viewpoint of customer behavioral analyze. This study presents a new two-stage frame-work of customer behavior analysis that integrated a K-means algorithm and Apriori association rule inducer. The K-means algorithm was used to identify groups of customers based on recency, frequency, monetary behavioral scoring predicators; it also divides customers into three major profitable groups of customers. Apriori association rule inducer was used to characterize the groups of customers by creating customer profiles. Identifying customers by a customer behavior analysis model is helpfulcharacteristics of customer and facilitates marketing strategy development.
کلیدواژه ها:
نویسندگان
Mohammad Ali Farajian
K.N.Toosi University of Technology, Tehran, Ira
Shahriar Mohammadi
K.N.Toosi University of Technology, Tehran, Iran