EFIM: a fast and memory efficient algorithm for high-utility itemset mining

Dynamic Memory Efficient Frequent Pattern Growth for Data Excavation

“…It also introduces Repeat Pattern Table (RPT) -a table to manage TPN. This procedure is designed with caution to handle computational resources [13][ like memory [14] and time [15] with improved basic metrics of a data mining procedure of accuracy, precision and recall. If the size of a node is declared as , then the size of the tree will be 30 .…”

Analyzing of incremental high utility pattern mining based on tree structures

“…Transaction utility (TU) of a transaction is a sum of utility values of all items in the transaction. There are recent studies such as the EFIM algorithm [15] to improve mining performance when discovering high utility patterns.…”

An efficient approach for mining closed high utility itemsets and generators

“…Sahoo et al (2015) proposed a Fast High Utility Itemset Miner (FHIM) and defined the promising utility co-occurrence structure to further reduce the number of candidates. Zida et al (2016) proposed the EFIM algorithm for fast HUIM. EFIM calculates the remaining utility (called the sub-tree utility) at the parent node rather than at child nodes in the depth-first search, and thus it can prune more nodes in the search space.…”

“…In HUIM, the utility of an itemset is neither monotonic nor anti-monotonic, and thus a HUI may have a superset or subset with lower, equal or higher utility. Therefore, we cannot apply some techniques on pruning the search space which have been developed in FIM into HUIM, and thus many recent algorithms have been developed focusing on mining HUIs, especially on candidate elimination (Liu et al, 2005;Liu & Qu, 2012;Tseng et al, 2013;Zida et al, 2016).…”

“…Many investigations of high utility itemset mining (HUIM) have been published, such as the incremental mining algorithm based on the concept of the fast-update approach for efficiently extracting high utility itemsets (HUIs) (Lin, Lan, Hong, 2012), mining HUIs from vertically distributed databases (Vo, Nguyen, & Le, 2009), the HUI-Miner algorithm with a utilitylist structure to store both utility and heuristic information for pruning the search space (Liu & Qu, 2012), the Efficient high-utility Itemset Mining (EFIM) algorithm using two upper bounds called sub-tree utility and local utility for search-space pruning (Zida, Fournier-Viger, Lin, Wu, & Tseng, 2016), mining top-k HUIs (Tseng, Wu, Fournier-Viger, & Yu, 2016), the UP-Growth algorithm using the UP-Tree data structure for candidate pruning (Tseng, Wu, Shie, & Yu, 2010), the UP-Growth+ algorithm (Tseng, Wu, Shie, & Yu, 2013), mining HUIs with multiple minimum utility thresholds (Gan, Lin, FournierViger, & Chao, 2016) and the Two-Phase algorithm for mining HUIs with effective performance (Liu, Liao, & Choudhary, 2005).…”