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

Yingquan Wang, Tomohiro Murata

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings of the International MultiConference of Engineers and Computer Scientists 2017, IMECS 2017
出版社Newswood Limited
ページ359-363
ページ数5
2227
ISBN(電子版)9789881404732
出版ステータスPublished - 2017 1 1
イベント2017 International MultiConference of Engineers and Computer Scientists, IMECS 2017 - Hong Kong, Hong Kong
継続期間: 2017 3 152017 3 17

Other

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

ASJC Scopus subject areas

  • コンピュータ サイエンス(その他)

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