DocumentCode :
447590
Title :
A new technique for fast frequent closed itemsets mining
Author :
Ning, Li ; Wu, Ningning ; Zhang, Jing
Author_Institution :
Dept. of Comput. Sci., Arkansas Univ., Little Rock, AR, USA
Volume :
4
fYear :
2005
fDate :
10-12 Oct. 2005
Firstpage :
3640
Abstract :
This paper proposes a new technique for fast frequent closed itemsets mining. The method employs the concept of prefix-based equivalence class to partition the database in such a way that the possible frequent itemsets will be uniformly distributed in the partitions. One unique property of this technique is that it guarantees only frequent closed itemsets are generated and they are generated only once. Thus it avoids the closed-set examination in the entire mining processing. Our technique can be used in the existing frequent closed itemsets mining algorithms, including both Apriori-based and frequent pattern growth based algorithms, to improve their performance. Moreover, the technique is well suited for parallel mining of frequent closed itemsets.
Keywords :
data mining; equivalence classes; association rule mining; closed-set examination; data mining; frequent closed itemsets mining; frequent pattern growth based algorithm; mining processing; parallel mining; prefix-based equivalence class; Association rules; Computer science; Data mining; Distributed databases; Information science; Itemsets; Iterative algorithms; Iterative methods; Partitioning algorithms; Transaction databases; association rule mining; data mining; frequent itemset mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 2005 IEEE International Conference on
Print_ISBN :
0-7803-9298-1
Type :
conf
DOI :
10.1109/ICSMC.2005.1571713
Filename :
1571713
Link To Document :
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