1 A Parameterised Algorithm for Mining Association Rul es Department of Information & Computer Education, NTNU Nuansri Denwattana, and Janusz R Getta, Datab ase Conference 2001 (ADC 2001) Proceedings. 12th Au stralasian, 29 Jan.-2 Feb. 2001, pp. 45-51. Advisor : Jia-Ling Koh Speaker : Chen-Yi Lin
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A Parameterised Algorithm for Mining Association Rules
Department of Information & Computer Education, NTNU. A Parameterised Algorithm for Mining Association Rules. Nuansri Denwattana, and Janusz R Getta, Database Conference 2001 (ADC 2001) Proceedings. 12th Australasian, 29 Jan.-2 Feb. 2001 , pp. 45-51. Advisor : Jia-Ling Koh - PowerPoint PPT Presentation
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1
A Parameterised Algorithm for Mining Association Rules
Department of Information & Computer Education, NTNU
Nuansri Denwattana, and Janusz R Getta, Database Conference 2001 (ADC 2001) Proceedings. 12th Australasian, 29 Jan.-2 Feb. 200
1, pp. 45-51.
Advisor: Jia-Ling Koh
Speaker: Chen-Yi Lin
2
Introduction Problem Definition Finding Frequent Itemsets Experimental Results Conclusion
Department of Information & Computer Education, NTNU
Outline
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Introduction (1/2)
Majority of the algorithms finding frequent itemsets counts one category of itemsets, e.g. Apriori algorithm.
The quality of association rule mining algorithms is determined:– the number of passes through an input dat
aset– the number of candidate itemsets
Department of Information & Computer Education, NTNU
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Introduction (2/2)
One of the objectives is to construct an algorithm that makes a good guess.– the parameterised (n, p) algorithm finds all
frequent itemsets from a range of n levels in itemset lattice in p passes (n>=p) through an input data set.
Department of Information & Computer Education, NTNU
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Problem Definition
Positive candidate itemset– It is assumed (guessed) to be frequent.
Negative candidate itemset– It is assumed (guessed) to be not frequent.
Remaining candidate itemset – candidates verified in another scan.
C
C
RC
Department of Information & Computer Education, NTNU
Department of Information & Computer Education, NTNU
Initial DBscan
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FEDCBAL ,,,,,1
Item frequency threshold = 80%m-element transaction threshold = 5Number of levels to traverse (n) = 3Number of passes through an input data set (p) = 2