Association Rule Mining with Data Item including Independency based on Enhanced Confidence Factor

Yingquan Wang, Tomohiro Murata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Along with the development of data collection and various storage technology, the large data of users activities in economy is stored. Extracting valuable information or knowledge regarding behavior of user from these data is becoming more and more important for marketing strategies of sales and commerce. Association rule mining is one of useful techniques in this application field and widely studied. But sometimes too many rules that generated by association rule mining usually caused the wrong decisions made by manager, parts of generated rules are meaningful and useful, but other generated rules are unnecessary for manager to make the right decisionsIn this paper, in order to extract useful rules efficiently, we proposed a new framework of association rule mining based on enhanced confidence factor. Thus, the certainty factor was introduced to identify different situations and analysis the accuracy of association rule mining respectively. We illustrate some merits of our proposed method by theoretical analysis. Our experiment results show that the sets of useful rules can be generated in a more efficient way by using our method, which means less and more accurate rules could be used to make the proper decisions by manager.

Original languageEnglish
Title of host publicationProceedings of the International MultiConference of Engineers and Computer Scientists 2017, IMECS 2017
PublisherNewswood Limited
Pages359-363
Number of pages5
Volume2227
ISBN (Electronic)9789881404732
Publication statusPublished - 2017 Jan 1
Event2017 International MultiConference of Engineers and Computer Scientists, IMECS 2017 - Hong Kong, Hong Kong
Duration: 2017 Mar 152017 Mar 17

Other

Other2017 International MultiConference of Engineers and Computer Scientists, IMECS 2017
CountryHong Kong
CityHong Kong
Period17/3/1517/3/17

Keywords

  • Association rule mining
  • Certainty factor
  • Independency
  • Negative dependence

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

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  • Cite this

    Wang, Y., & Murata, T. (2017). Association Rule Mining with Data Item including Independency based on Enhanced Confidence Factor. In Proceedings of the International MultiConference of Engineers and Computer Scientists 2017, IMECS 2017 (Vol. 2227, pp. 359-363). Newswood Limited.