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Context of the concept drift in data mining: an empirical study on the regional economic influence to the relation between demographic attributes and credit card holder's loyalty

  • Jia Hui Mo*
  • , Peng Zou
  • , Jin Chen
  • *Corresponding author for this work
  • School of Management, Harbin Institute of Technology
  • Renmin University of China

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In data mining field, concept drift means that the original rule can not adapt to the sample. Concept drift will make the rules unable to be used widely. Recent works supposed the concept drift was influenced by many hidden factors which is called context factor, but did not give evidence. Based on the concept drift theories, this paper carried on the empirical study with Chinese credit card holders, finding the regional economic environment is a remarkable context factor to the individual loyal behavior; it hidden influenced the relation between individual characteristic and their behavior indirectly. This research offered a real empirical example for the influence of context factors of concept drift and proposed a special perspective for Chinese credit card market.

Original languageEnglish
Title of host publication2008 International Conference on Management Science and Engineering 15th Annual Conference Proceedings, ICMSE
Pages38-43
Number of pages6
DOIs
StatePublished - 2008
Externally publishedYes
Event2008 International Conference on Management Science and Engineering 15th Annual Conference, ICMSE - Long Beach, CA, United States
Duration: 10 Sep 200812 Sep 2008

Publication series

Name2008 International Conference on Management Science and Engineering 15th Annual Conference Proceedings, ICMSE

Conference

Conference2008 International Conference on Management Science and Engineering 15th Annual Conference, ICMSE
Country/TerritoryUnited States
CityLong Beach, CA
Period10/09/0812/09/08

Keywords

  • Concept drift
  • Context factor
  • Customer loyalty
  • Data mining

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