A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets

Traditional association rules mining cannot meet the demands arising from some real applications. By considering the different values of individual items as utilities, utility mining focuses on identifying the itemsets with high utilities. In this paper, we present a Two-Phase algorithm to efficiently prune down the number of candidates and precisely obtain the complete set of high utility itemsets. It performs very efficiently in terms of speed and memory cost both on synthetic and real databases, even on large databases that are difficult for existing algorithms to handle.

[1]  Fan Li,et al.  Mining weighted association rules , 2001, Intell. Data Anal..

[2]  Ada Wai-Chee Fu,et al.  Mining association rules with weighted items , 1998, Proceedings. IDEAS'98. International Database Engineering and Applications Symposium (Cat. No.98EX156).

[3]  Philip S. Yu,et al.  Efficient mining of weighted association rules (WAR) , 2000, KDD '00.

[4]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[5]  Qiang Yang,et al.  Mining high utility itemsets , 2003, Third IEEE International Conference on Data Mining.

[6]  Howard J. Hamilton,et al.  Extracting Share Frequent Itemsets with Infrequent Subsets , 2003, Data Mining and Knowledge Discovery.

[7]  Fionn Murtagh,et al.  Weighted Association Rule Mining using weighted support and significance framework , 2003, KDD '03.

[8]  Cory J. Butz,et al.  A Foundational Approach to Mining Itemset Utilities from Databases , 2004, SDM.

[9]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.