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Title page for etd-0803105-170901


URN etd-0803105-170901 Statistics This thesis had been viewed 1606 times. Download 15 times.
Author Chia-hui Zhuang
Author's Email Address No Public.
Department Information Management
Year 2004 Semester 2
Degree Master Type of Document Master's Thesis
Language English Page Count 44
Title Mining Quantitative Association Rules with Multiple Minimum Supports
Keyword
  • quantitative association rule
  • data mining
  • association rule
  • association rule
  • data mining
  • quantitative association rule
  • Abstract The past research of mining quantitative association rules is aim to use fuzzy value like large quantity, small quantity, etc. to express quantitative attribute. It is difficult to design a bundle of items for sales promotion. In Chen’s paper [3], an Apriori-based algorithm, named MQA-1, is developed to mine association rules in bag database. However, using only one minimum support can’t reflect the nature of items.
    In this paper, we propose a FP-tree-like structure to store all information about itembag and an efficient algorithm to mine quantitative association rules with multiple minimum supports. It form looks like “milk = 2 ? bread = 3”, then we can combine three units of milk with two units of bread to form bundling. For decision makers, it is easy and precise to make decisions with quantity information.
    Advisor Committee
  • Yen-ju Yang - advisor
  • Huei-huang Chen - co-chair
  • Shih-sheng Chen - co-chair
  • Files indicate in-campus access only
    Date of Defense 2005-07-04 Date of Submission 2005-08-03


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