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A Study for Discovery of Distorter Variable Using Association Rules
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연관성 규칙을 이용한 왜곡변수 발견에 관한 연구

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Type
Academic journal
Author
Journal
The Korean Data Analysis Society Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제9권 제2호 KCI Accredited Journals
Published
2007.1
Pages
711 - 719 (9page)

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A Study for Discovery of Distorter Variable Using Association Rules
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Abstract· Keywords

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An important goal of data mining is to discover, define and determine the relationship between several variables. These variables, in turn, represent some levers and mechanisms that move business operations. A variety of pitfalls relating to extraneous, hidden and distorter variables can result in misunderstandings, inaccurate outputs and conclusions. In this paper we propose association rules based on distorter variables. We call these rules to distorter association rules. Distorter variable is a variable that reveals that the correct interpretation is precisely the reverse of that suggested by the original data. For example, a distorter variable is exemplified by the apparent theory that more married people commit suicide than single people. However, when the population is segmented by age, it is found that in each age group, the single suicides outnumber the married. Therefore, this actually supports the thesis that marriage reduces suicide.
#신뢰도 #연관성규칙 #왜곡변수 #왜곡 연관 규칙 #지지도 #향상도. #association rules #confidence #distorter variable #distorter association rule #lift #support.

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References (11)

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Agrawal R. / 1993 / Mining association rules between sets of items in large databases / Proc. of the ACM SIGMOD Conference on Management of Data : 207 ~ 216 google schola Agrawal R. / 1994 / Fast algorithms for mining association rules / Proc. of the 20th VLDB Conference : 487 ~ 499 google schola Cheung D. W. / 1996 / A Fast distribution algorithm for mining association rules / Int's Conf. on Parallel and Distributes Information System : 31 ~ 43 google schola Park J. S. / 1995 / An effective hash-based algorithms for mining association rules / Proc. of ACM SIGMOD Conference on Management of Data. : 175 ~ 186 google schola Saygin Y. / 2002 / Using Unknowns to Prevent Discovery of Association Rules / 2002 Conference on Research Issues in Data Engineering : 45 ~ 54 google schola

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