Abstract
High-utility pattern mining is a research hotspot in the field of pattern mining, and one of its main research topics is how to improve the efficiency of the mining algorithm. Based on the study on the state-of-the-art high-utility pattern mining algorithms, this paper proposes an improved strategy that removes noncandidate items from the global header table and local header table as early as possible, thus reducing search space and improving efficiency of the algorithm. The proposed strategy is applied to the algorithm EFIM (EFficient high-utility Itemset Mining). Experimental verification was carried out on nine typical datasets (including two large datasets); results show that our strategy can effectively improve temporal efficiency for mining high-utility patterns.
Highlights
Mathematical Problems in Engineering generating candidate itemsets
There is a new fast mining algorithm EFIM [19] proposed by Zida et al, in which two new upper bounds of utility value are used to reduce the search space and boost the performance greatly. e algorithm HMiner [20] proposed two pruning techniques, LAprune and C-prune, to reduce the search space for mining HUPs. e algorithm ULB-Miner [21] extended the algorithms FHM [18] and HUI-Miner [16] by utilizing a utility list buffer structure, which improved the performance of the FHM algorithm in terms of the memory and runtime usage
An improved strategy is proposed to boost the temporal efficiency of HUP mining algorithm and is applied to the algorithm EFIM
Summary
Mathematical Problems in Engineering generating candidate itemsets. Comparing with the twophase HUP mining algorithms, the speeds of algorithms d2HUP and HUI-Miner are greatly improved.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
