• Title/Summary/Keyword: Association rule ranking function

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Association rule ranking function using conditional probability increment ratio (조건부 확률증분비를 이용한 연관성 순위 결정 함수)

  • Park, Hee-Chang
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.4
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    • pp.709-717
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    • 2010
  • The task of association rule mining is to find certain association relationships among a set of data items in a database. There are three primary measures for association rule, support and confidence and lift. In this paper we developed a association rule ranking function using conditional probability increment ratio. We compared our function with several association rule ranking functions by some numerical examples. As the result, we knew that our decision function was better than the existing functions. The reasons were that the proposed function of the reference value is not affected by a particular association threshold, and our function had a value between -1 and 1 regardless of the range for three association thresholds. And we knew that the ranking function using conditional probability increment ratio was very well reflected in the difference between association rule measures and the minimum association rule thresholds, respectively.

Association rule ranking function by decreased lift influence (향상도 영향 감소화에 의한 연관성 순위결정함수)

  • Park, Hee-Chang
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.3
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    • pp.397-405
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    • 2010
  • Data mining is the method to find useful information for large amounts of data in database, and one of the important goals is to search and decide the association for several variables. The task of association rule mining is to find certain association relationships among a set of data items in a database. There are three primary measures for association rule, support and confidence and lift. In this paper we developed a association rule ranking function by decreased lift influence to generate association rule for items satisfying at least one of three criteria. We compared our function with the functions suggested by Park (2010), and Wu et al. (2004) using some numerical examples. As the result, we knew that our decision function was better than the function of Park's and Wu's functions because our function had a value between -1 and 1regardless of the range for three association thresholds. Our function had the value of 1 if all of three association measures were greater than their thresholds and had the value of -1 if all of three measures were smaller than the thresholds.