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Author:

Liu, Yunfeng (Liu, Yunfeng.) | Wang, Xiaohui (Wang, Xiaohui.) | Zhai, Dongsheng (Zhai, Dongsheng.) (Scholars:翟东升)

Indexed by:

EI Scopus

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. © 2010 IEEE.

Keyword:

Clustering algorithms Statistics Genetic algorithms Sales Data mining

Author Community:

  • [ 1 ] [Liu, Yunfeng]Economics and Management School, Beijing University of Technology, Beijing 100124, China
  • [ 2 ] [Wang, Xiaohui]Economics and Management School, Beijing University of Technology, Beijing 100124, China
  • [ 3 ] [Zhai, Dongsheng]Economics and Management School, Beijing University of Technology, Beijing 100124, China

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Source :

Year: 2010

Volume: 1

Page: 164-166

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 12

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