CWFM: closed contingency weighted frequent itemsets mining

Authors:
Eunkyoung Park;Younghee Kim;Ieejoon Kim;Jaeyeol Yoon;Jiyeon Lim;Ungmo Kim
Affiliations:
Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea;Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea;Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea;Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea;Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea;Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea
Venue:
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
Year:
2012

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Abstract

Weighted pattern mining have been studied the importance of items. So far, in weight constraint based pattern mining, the weight has been considered the item's price. The price considered as the weight has a limit. The weight characteristic of weighted pattern mining should be considered case-by-case situation. Thus, we motivate by considering the special and individual case-by-case situation to find the exact frequent patterns. We propose how to set weight into frequent patterns mining with a case-by-case condition, called CWFM (closed contingency weighted pattern miming). Moreover, we devise information tables by using statistical and empirical data as strategic decision. In addition, we calculate the contingency weight using outer variables and values which are from information tables. CWFM extracts more meaningful and appropriate patterns reflected case-by-case situation. The proposed new mining method finds closed contingency weighted frequent patterns having a significance which represents the case-by-case situation.