An efficient approach for mining closed high utility itemsets and generators

Mai, Thang; Nguyen, Loan T. T.

doi:10.1080/24751839.2017.1347392

Cited by 6 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their approach [35] involved constructing a lattice structure of HUIs, and then using the LARM algorithm to generate HARs. The lattice approach was proved to be efficient for mining frequently closed itemsets [34] and closed high-utility itemsets [36], mining generalized association rules [37], mining non-redundant association rules [38], and mining association rules [39]. With regard to HUIM, LARM is the first algorithm that applies the lattice approach and it has shown good performance in the mining of HARs, especially when used on large datasets.…”

Section: High-utility Association Rule Miningmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Algorithm for Mining Non-Redundant High-Utility Association Rules

Mai

Nguyen

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

In business, managers may use the association information among products to define promotion and competitive strategies. The mining of high-utility association rules (HARs) from high-utility itemsets enables users to select their own weights for rules, based either on the utility or confidence values. This approach also provides more information, which can help managers to make better decisions. Some efficient methods for mining HARs have been developed in recent years. However, in some decision-support systems, users only need to mine a smallest set of HARs for efficient use. Therefore, this paper proposes a method for the efficient mining of non-redundant high-utility association rules (NR-HARs). We first build a semi-lattice of mined high-utility itemsets, and then identify closed and generator itemsets within this. Following this, an efficient algorithm is developed for generating rules from the built lattice. This new approach was verified on different types of datasets to demonstrate that it has a faster runtime and does not require more memory than existing methods. The proposed algorithm can be integrated with a variety of applications and would combine well with external systems, such as the Internet of Things (IoT) and distributed computer systems. Many companies have been applying IoT and such computing systems into their business activities, monitoring data or decision-making. The data can be sent into the system continuously through the IoT or any other information system. Selecting an appropriate and fast approach helps management to visualize customer needs as well as make more timely decisions on business strategy.

show abstract

Section: High-utility Association Rule Miningmentioning

confidence: 99%

“…Each node represents an itemset, utility, support, "closed" flag and "generator" flag [36]. The utility and support values of the root node are equal to zero.…”

Section: Mining Nr-hars From a Lattice Of High-utility Itemsetsmentioning

confidence: 99%

Efficient Algorithm for Mining Non-Redundant High-Utility Association Rules

Mai

Nguyen

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since its first introduction in 1993 [1] it has attracted a lot of attention and has been extended and applied in various ways. For instance, some popular variations of the FI mining problem are to discover high utility patterns [2,3], uncertain frequent patterns [2] and high utility association rules [2,4]. Most algorithms for mining FIs partition the search space into subclasses in order to apply the parallel approaches to improve their performance.…”

Section: Introductionmentioning

confidence: 99%

NUCLEAR: An Efficient Methods for Mining Frequent Itemsets and Generators from Closed Frequent Itemsets

Pham

Tran²,

Duong³

et al. 2021

ITII

View full text Add to dashboard Cite

Frequent itemset (FI) mining is an interesting data mining task. Instead of directly mining the FIs from data it is preferred to mine only the closed frequent itemsets (CFIs) first and then extract the FIs for each CFI. However, some algorithms require the generators for each CFI in order to extract the FIs, leading to an extra cost. In this paper, we introduce an effective algorithm, called NUCLEAR, which can induce the FIs from the lattice of CFIs without the need of the generators. It can enumerate generators as well by similar fashion. Experimental results showed that NUCLEAR is effective as compared to previous studies, especially, the time for extracting the FIs is usually much smaller than that for mining the CFIs.

show abstract

Efficient algorithms for mining closed high utility itemsets in dynamic profit databases

Nguyen

et al. 2021

Expert Systems with Applications

View full text Add to dashboard Cite

An efficient approach for mining closed high utility itemsets and generators

Cited by 6 publications

References 19 publications

Efficient Algorithm for Mining Non-Redundant High-Utility Association Rules

Efficient Algorithm for Mining Non-Redundant High-Utility Association Rules

NUCLEAR: An Efficient Methods for Mining Frequent Itemsets and Generators from Closed Frequent Itemsets

Efficient algorithms for mining closed high utility itemsets in dynamic profit databases

Contact Info

Product

Resources

About