FHUQI-Miner: Fast high utility quantitative itemset mining

Nouioua, Mourad; Fournier‐Viger, Philippe; Wu, Chengwei; Lin, Jerry Chun‐Wei; Gan, Wensheng

doi:10.1007/s10489-021-02204-w

Cited by 25 publications

(6 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…High-Utility Itemset (HUI) is one of the most widely used Data Mining Tasks in recent years due to its application in various fields [40]. HUI mining is used to identify itemset that have a high utility such as profit in the transactional database [38].…”

Section: A High Utility Itemsetmentioning

confidence: 99%

“…The next development in High-Utility Mining is the consideration that the number of items purchased has a large influence on more accurate decision-making [40]. Therefore, in the process of determining the High-Utility Itemset, the number of items purchased is involved as the basis for calculating the utility.…”

Section: A High Utility Itemsetmentioning

confidence: 99%

“…The detailed memory usage for each model and dataset is shown in Figure 10. Experiments were also carried out to compare the proposed model with the FHUQI Miner algorithm [40] using the same 8 datasets. The results of the experiment can be seen in table XIII.…”

Section: Figure 7 Comparison Of Runtime Adaptive Rules Adr-au and Adr...mentioning

confidence: 99%

See 2 more Smart Citations

Pruning Strategy on Adaptive Rule Model by Sorting Utility Items

2022

View full text Add to dashboard Cite

The adaptive Rule Model is an association rule development that formulates a minimum threshold value according to the data characteristics. The formulation process is based on item frequency and utility for other considerations, which requires high runtime and memory usage. For better performance, it is necessary to develop a pruning strategy that can minimize runtime and memory usage without reducing the quality of the rule produced. Therefore, this research aims to develop 2 pruning strategies, namely the Adaptive Rule with Average Utility (ADR-AU) and the Adaptive Rule with Sorting Utility (ADR-SU) to improve the adaptive rule model. The ADR-AU changes the minimum threshold using the average utility item. The ADR-SU model proposes an additional step in the frequent itemset determination process. It is used for sorting utility items in descending order before the iteration process is carried out to compare whether the values are greater than the minimum threshold. Based on the experiments on 8 datasets, the number of frequent itemset in the ADR-AU and ADR-SU models was significantly less in the adaptive rule model. The results showed that all datasets experienced a decrease in runtime between the adaptive rule model and ADR-AU. Meanwhile, for the Zoo and Sco datasets, there was an increase in runtime between the ADR-AU and ADR-SU models. In terms of memory usage, the connect and retail datasets experienced a decrease, while the other 4 were increased at the ADR-AU and ADR-SU stages.

show abstract

Section: A High Utility Itemsetmentioning

confidence: 99%

Section: A High Utility Itemsetmentioning

confidence: 99%

See 1 more Smart Citation

Pruning Strategy on Adaptive Rule Model by Sorting Utility Items

2022

View full text Add to dashboard Cite

show abstract

“…While regular mining of patterns can be useful, it is supposed to be interesting frequent patterns. However, there are also applications of this assumption has been discussed in [5].…”

Section: Introductionmentioning

confidence: 99%

Yüksek Faydalı Öğe Kümeleri için Veri Madenciliği Algoritmalarının Analizi Üzerine Bir Anket

NELLUTLA>

Srinivasan²

2022

ECJSE

View full text Add to dashboard Cite

High-Utility-Itemset Mining (HUIM) is meant to detect extremely important trends by considering the purchasing quantity and product benefits of items. For static databases, most of the measurements are expected. In real time applications, such as the market basket review, company decision making and web administration organization results, large quantities of datasets are slowly evolving with new knowledge incorporated. The usual mining calculations cannot handle such complex databases and retrieve useful data. The essential task of data collection in a quantifiable sequence dataset is to determine entirely high utility sequences. The number of sequences found is always extremely high, though useful. This article studies the issue of the mining of repeated high utility sequence that meet item restrictions in order to identify patents that are more suited to the needs of a customer. Also, this article introduces high-value element set mining, examines modern algorithms, their extensions, implementations, and explores research opportunities.

show abstract

“…In the data mining field [1], [2], [3], closed itemset mining [4] has been developed for a long time and been the focus of many studies [5], [6], [7], [8], [9], [10]. But in the graph mining field [11], [12], [13], [14], closed frequent subgraph mining is a new problem with little research [15], [16], [17], [18], [19].…”

Section: Introductionmentioning

confidence: 99%

A Method for Closed Frequent Subgraph Mining in a Single Large Graph

Nguyen

Zelinka

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Mining frequent subgraphs is an interesting and important problem in the graph mining field, in that mining frequent subgraphs from a single large graph has been strongly developed, and has recently attracted many researchers. Among them, MNI-based approaches are considered as state-of-the-art, such as the GraMi algorithm. Besides frequent subgraph mining (FSM), frequent closed frequent subgraph mining was also developed. This has many practical applications and is a fundamental premise for many studies. This paper proposes the CloGraMi (Closed Frequent Subgraph Mining) algorithm based on GraMi to find all closed frequent subgraphs in a single large graph. Two effective strategies are also developed, the first one is a new level order traversal strategy to quickly determine closed subgraphs in the searching process, and the second is setting a condition for early pruning a large portion of non-closed candidates, both of them aim to reduce the running time as well as the memory requirements, improve the performance of the proposed algorithm. Our experiments are performed on five real datasets (both directed and undirected graphs) and the results show that the running time as well as the memory requirements of our algorithm are significantly better than those of the GraMi-based algorithm.INDEX TERMS data mining; frequent closed subgraph; social network; pruning strategy.

show abstract

FHUQI-Miner: Fast high utility quantitative itemset mining

Abstract: This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature's AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections.

Cited by 25 publications

References 36 publications

Pruning Strategy on Adaptive Rule Model by Sorting Utility Items

Pruning Strategy on Adaptive Rule Model by Sorting Utility Items

Yüksek Faydalı Öğe Kümeleri için Veri Madenciliği Algoritmalarının Analizi Üzerine Bir Anket

A Method for Closed Frequent Subgraph Mining in a Single Large Graph

Contact Info

Product

Resources

About