Title :
A Constrained Maximum Frequent Itemsets Incremental Mining Algorithm
Author :
Wang, Han ; Kong, Lingfu
Author_Institution :
Yanshan Univ., Qinhuangdao
Abstract :
Among all data mining algorithms of association rules, incremental algorithms fit dataset updating better. This paper proposes a novel algorithm of mining the constrained maximum frequent itemsets namely algorithm ISL-DM. This algorithm filters the item-sequences which can not get or become the maximum frequent itemsets by the constraint conditions, and it can always surround getting the maximum frequent itemsets currently.
Keywords :
data mining; association rules; constrained maximum frequent itemsets; data mining algorithms; incremental mining algorithm; Association rules; Computer networks; Concurrent computing; Data engineering; Data mining; Filters; Itemsets; Lattices; Parallel processing; Transaction databases;
Conference_Titel :
Network and Parallel Computing Workshops, 2007. NPC Workshops. IFIP International Conference on
Conference_Location :
Liaoning
Print_ISBN :
978-0-7695-2943-1
DOI :
10.1109/NPC.2007.110
