| dc.description.abstract | High-utility itemset mining (HUIM) has emerged as a crucial study area in recent decades, as
both quantity and profit variables are important when mining high-utility itemsets (HUIs).
Ever since then, there have been several similar algorithms proposed. In 2012, Liu and Qu
presented the HUI-Miner method (High Utility Itemset Miner) to exploit high utility itemsets
by storing all valuable information about an itemset in a new structure called a utility list.
The HUI-Miner method is believed to be an effective technique for extracting high utility
itemsets. Later, the FHM method used a utility list structure to minimize search space.
However, the database is always evolving since fresh data is collected regularly, coal... To
leverage HUI on a growing database, an efficient technique is required. The EIHI method
presented in 2015 is a very efficient item-mining technique applicable to the growing
database, the algorithm used a tree-typed data structure to perform storing and updating
whenever a new itemset is founded, this process can cause certain drawbacks in the algorithm,
especially when dealing with big data. In this paper, I will focus on the procedure of
organizing itemset during the mining of the EIHI algorithm and suggest a better data
structure to improve the performance of EIHI algorithm. | en_US |
