DocumentCode :
1866173
Title :
Efficiently Using Matrix in Mining Maximum Frequent Itemset
Author :
Zhen-yu, Liu ; Wei-xiang, Xu ; Xumin, Liu
Author_Institution :
Sch. of Traffic & Transp., Beijing Jiaotong Univ., Beijing, China
fYear :
2010
fDate :
9-10 Jan. 2010
Firstpage :
50
Lastpage :
54
Abstract :
An efficient way to discover the maximum frequent itemsets can be very useful for mining association rules, correlations, episodes patterns, etc. Most existing work focuses on the technique for mining candidate maximal frequent itemsets and ignores the technique for MFI checking. However the efficient of a MFS mining algorithm lies on these two parts. In this paper, a new MFI checking method is presented based on the optimizing of the former called MaxMatrix and an additional constraint for association rules generating is discussed to save mining time. In order to understand the process of MaxMatrix easily, an example is provided in detail.
Keywords :
data mining; matrix algebra; set theory; MFI checking; MFS mining algorithm; MaxMatrix; episodes patterns; mining association rules; mining candidate maximal frequent itemsets; Association rules; Constraint optimization; Data engineering; Data mining; Databases; Itemsets; Knowledge engineering; Pattern recognition; Testing; Transportation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Knowledge Discovery and Data Mining, 2010. WKDD '10. Third International Conference on
Conference_Location :
Phuket
Print_ISBN :
978-1-4244-5397-9
Electronic_ISBN :
978-1-4244-5398-6
Type :
conf
DOI :
10.1109/WKDD.2010.27
Filename :
5432736
Link To Document :
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