FIUT: A new method for mining frequent itemsets
作者： Yuh-Jiuan Tsay , Tain-Jung Hsu , Jing-Rung Yu
DOI: 10.1016/J.INS.2009.01.010
关键词: Traverse 、 Database transaction 、 Partition (database) 、 Cluster analysis 、 Mathematics 、 Data mining 、 Ultrametric space
摘要: This paper proposes an efficient method, the frequent items ultrametric trees (FIUT), for mining itemsets in a database. FIUT uses special tree (FIU-tree) structure to enhance its efficiency obtaining itemsets. Compared related work, has four major advantages. First, it minimizes I/O overhead by scanning database only twice. Second, FIU-tree is improved way partition database, which results from clustering transactions, and significantly reduces search space. Third, each transaction are inserted as nodes into compressed storage. Finally, all generated checking leaves of FIU-tree, without traversing recursively, computing time. was compared with FP-growth, well-known widely used algorithm, simulation showed that outperforms FP-growth. In addition, further extensions this approach their implications discussed.
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sci-hub.se 参考文章(21)
Jian Pei, Jiawei Han, Hongjun Lu, Shojiro Nishio, Shiwei Tang, Dongqing Yang, H-mine: hyper-structure mining of frequent patterns in large databases international conference on data mining. pp. 441- 448 ,(2001) , 10.1109/ICDM.2001.989550
Ramakrishnan Srikant, Rakesh Agrawal, Fast Algorithms for Mining Association Rules in Large Databases very large data bases. pp. 487- 499 ,(1994)
Shamkant B. Navathe, Edward Omiecinski, Ashoka Savasere, An Efficient Algorithm for Mining Association Rules in Large Databases very large data bases. pp. 432- 444 ,(1995)
David W Cheung, Jiawei Han, Vincent T Ng, Ada W Fu, Yongjian Fu, None, A fast distributed algorithm for mining association rules international conference on parallel and distributed information systems. pp. 31- 43 ,(1996) , 10.1109/PDIS.1996.568665
Michal Parnas, Dana Ron, Testing metric properties Information & Computation. ,vol. 187, pp. 155- 195 ,(2003) , 10.1016/S0890-5401(03)00160-3
Jie Dong, Min Han, BitTableFI: An efficient mining frequent itemsets algorithm Knowledge Based Systems. ,vol. 20, pp. 329- 335 ,(2007) , 10.1016/J.KNOSYS.2006.08.005
Yuh-Jiuan Tsay, Ya-Wen Chang-Chien, An efficient cluster and decomposition algorithm for mining association rules Information Sciences. ,vol. 160, pp. 161- 171 ,(2004) , 10.1016/J.INS.2003.08.013
Shichao Zhang, Jilian Zhang, Chengqi Zhang, EDUA: An efficient algorithm for dynamic database mining Information Sciences. ,vol. 177, pp. 2756- 2767 ,(2007) , 10.1016/J.INS.2007.01.034
Jong Soo Park, Ming-Syan Chen, Philip S. Yu, An effective hash-based algorithm for mining association rules international conference on management of data. ,vol. 24, pp. 175- 186 ,(1995) , 10.1145/223784.223813
Ramesh C. Agarwal, Charu C. Aggarwal, V.V.V. Prasad, A Tree Projection Algorithm for Generation of Frequent Item Sets Journal of Parallel and Distributed Computing. ,vol. 61, pp. 350- 371 ,(2001) , 10.1006/JPDC.2000.1693