• Title/Summary/Keyword: Boolean Analyzer

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A Study for Statistical Criterion in Negative Association Rules Using Boolean Analyzer

  • Lee, Keun-Woo;Park, Hee-Chang
    • Journal of the Korean Data and Information Science Society
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    • v.19 no.2
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    • pp.569-576
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    • 2008
  • Association rule mining searches for interesting relationships among items in a given database. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. Association rule is an interesting rule among purchased items in transaction, but the negative association rule is an interesting rule that includes items which are not purchased. Boolean Analyzer is the method to produce the negative association rule using PIM. But, PIM is subjective. In this paper, we present statistical objective criterion in negative association rules using Boolean Analyzer.

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Finding negative association rules with Boolean Analyzer (Boolean Analyzer를 이용한 역 연관규칙의 발견)

  • Lee, Jong-In;Park, Sang-Ho;Kang, Yun-Hee;Park, Sun;Lee, Ju-Hong
    • Proceedings of the Korean Information Science Society Conference
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    • 2003.10a
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    • pp.187-189
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    • 2003
  • 연관 규칙이 구매한 항목에 관심을 가져 구매 항목간의 규칙을 생성하는 것이라면 역 연관규칙은 구매하지 않은 항목에도 관심을 가짐으로써 더욱 효과적으로 데이터 마이닝을 하려는 시도이다. 역 연관규칙을 찾기 위한 기존의 방법들은 규칙의 일부분만 찾거나. 연관규칙을 찾는 알고리즘보다 더 복잡한 알고리즘의 사용으로 역 연관규칙을 찾는데 어려움이 있다. 이에 본 논문에서는 ITEM들 사이의 dependency를 이용하는 Boolean Analyzer를 사용하여 보다 간단한 과정으로 역 연관규칙을 생성하는 방법을 제시하고, 실험을 통하여 Boolean Analyzer로 역 연관규칙을 찾고 다른 알고리즘과 비교를 통해 보다 다양한 규칙을 찾을 수 있음을 보여준다.

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A Study for Statistical Criterion in Negative Association Rules Using Boolean Analyzer

  • Shin, Sang-Jin;Lee, Keun-Woo
    • 한국데이터정보과학회:학술대회논문집
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    • 2006.11a
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    • pp.145-151
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    • 2006
  • Association rule mining searches for interesting relationships among items in a given database. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule support and confidence and lift. Association rule is an interesting rule among purchased items in transaction, but the negative association rule is an interesting rule that includes items which are not purchased. Boolean Analyzer is the method to produce the negative association rule using PIM. But PIM is subjective. In this paper, we present statistical objective criterion in negative association rules using Boolean Analyzer.

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Finding Negative Association Rules in Implicit Knowledge Domain (함축적 지식 영역에서 부 연관규칙의 발견)

  • Park, Yang-Jae
    • The Journal of Information Technology
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    • v.9 no.3
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    • pp.27-32
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    • 2006
  • If is interested and create rule between it in item that association rules buys, by negative association rules is interested to item that do not buy, it is attempt to do data Maining more effectively. It is difficult that existent methods to find negative association rules find one part of rule, or negative association rules because use more complicated algorithm than algorithm that find association rules. Therefore, this paper presents method to create negative association rules by simpler process using Boolean Analyzer that use dependency between items. And as Boolean Analyzer through an experiment, show that can find negative association rules and more various rule through comparison with other algorithm.

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