Title :
Using HMT and HASH_TREE to Optimize Apriori Algorithm
Author :
Zeng, Zhiyong ; Yang, Hui ; Feng, Tao
Author_Institution :
Inf. Sch., Yunnan Univ. of Finance & Econ., Kunming, China
Abstract :
On the basis of deep analysis to the Apriori algorithm. In this paper, the HMT (HASH MAPPING TABLE) and HASH_TREE methodologies are used to optimize space complexity and time complexity. Using the HMT compressed Item sets, HASH_TREE can decentralize support count process. The result of experimental show that, space complexity and time complexity of Apriori algorithm is Efficiency reduced by using HMT and HASH_TREE.
Keywords :
computational complexity; data mining; file organisation; HASH_TREE methodology; HMT compressed Item set; HMT methodology; apriori algorithm; association rule; data mining; hash mapping table; space complexity; time complexity; Algorithm design and analysis; Association rules; Complexity theory; Economics; Finance; Itemsets; Apriori algorithm; Data mining; HASH_TREE; association rules; mapping-table;
Conference_Titel :
Business Computing and Global Informatization (BCGIN), 2011 International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4577-0788-9
Electronic_ISBN :
978-0-7695-4464-9
DOI :
10.1109/BCGIn.2011.109
