Title :
Commercial Banks Exceptional Client Distinguish Based on Data Mining
Author :
Liu Yunfeng ; Wang Xiaohui ; Zhai Dongsheng
Author_Institution :
Econ. & Manage. Sch., Beijing Univ. of Technol., Beijing, China
Abstract :
The commercial banks need identify exceptional client in their large number of customers to prevent abnormal customer´s risk. In this paper, four types of abnormal data detection method is introduced, present a new method- the k-medoids clustering algorithm combining genetic algorithm to detect the outlier. Finally, apply the algorithm to analysis credit data sets, detect outlier and identify abnormal customer..
Keywords :
banking; customer profiles; data mining; genetic algorithms; pattern clustering; abnormal data detection method; commercial banks exceptional client identification; credit data analysis; customers risk; data mining; genetic algorithm; k-medoids clustering algorithm; Algorithm design and analysis; Clustering algorithms; Convergence; Data mining; Economics; Encoding; Optimization; abnormal customer; commercial banks; genetic algorith; the k-medoids clustering algorithm;
Conference_Titel :
Information Technology and Applications (IFITA), 2010 International Forum on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-7621-3
Electronic_ISBN :
978-1-4244-7622-0
DOI :
10.1109/IFITA.2010.337
