A Multi-Core Approach to Efficiently Mining High-Utility Itemsets in Dynamic Profit Databases

Vo, Bay; Nguyen, Loan T. T.; Nguyen, Trinh Duy; Fournier-Viger, Philippe; Yun, Unil

doi:10.1109/access.2020.2992729

Cited by 28 publications

(11 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The basic idea is by dividing the search spaces that can be mined concurrently. CHUI-Mine [17], pEFIM [18] and MCH-Miner [19] are algorithms that implemented parallel processing to mine HUI. pEFIM which is extension from EFIM [20] and MCH-Miner which is extension from iMEFIM [21] are the efficient two that apply simple load balancing to manage the resources.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Hybrid implementation [15,16] is also done to combine advantages from different data structures to improve the performance. Moreover computation technology such as multicore processor can be used as a powerful infrastructure to execute many tasks in a concurrent time that leads to efficient mining process [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…PLB introduces new strategy by applying load balancing and more efficient memory management. PLB limits the number of task pool based on the number of available processors[19]. It also creates and limits the number of local ULB to improve the memory efficiency during exploration.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Parallel Algorithm to Efficiently Mine High Utility Itemset

Atmaja

Sonawane²

2022

ICT Analysis and Applications

View full text Add to dashboard Cite

Finding high utility itemset (HUI) from transactional databases like customer transaction data is not an easy task. The generated HUI should represent the real world condition which contains set of items that likely to be purchased together and also has high profit. Many algorithms have been introduced to overcome that issue. Parallelism is one of the good architectures that can be adapted for mining algorithms and it is rarely used for HUI Mining. The basic idea that can be adapted is by dividing the mining process based on sub search spaces in concurrent time to boost the performance. This paper proposed two new frameworks that adapted parallel mining namely CLB and PLB which are extension of ULB-Miner algorithm. CLB is hybrid algorithm from CTU-PRO and ULB-Miner, and PLB is proposed because of inefficient process in CLB. From the experimental evaluation, PLB outperforms ULB-Miner in all cases regarding execution time.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parallel Algorithm to Efficiently Mine High Utility Itemset

Atmaja

Sonawane²

2022

ICT Analysis and Applications

View full text Add to dashboard Cite

show abstract

“…Based on existing research on HUI mining, several variant algorithms have been proposed, e.g., high average-utility mining [26][27][28], Top-K high utility mining [29,30], HUI mining from data stream [31,32], high-utility association rules [33], multi-core or parallel mining [34,35], and HUIM over multiple data sources [36]. Most of these studies mainly apply methods of one phase or two phase.…”

Section: Variant Algorithmsmentioning

confidence: 99%

HUIL-TN & HUI-TN: Mining high utility itemsets based on pattern-growth

Wang

2021

PLoS ONE

View full text Add to dashboard Cite

In recent years, high utility itemsets (HUIs) mining has been an active research topic in data mining. In this study, we propose two efficient pattern-growth based HUI mining algorithms, called High Utility Itemset based on Length and Tail-Node tree (HUIL-TN) and High Utility Itemset based on Tail-Node tree (HUI-TN). These two algorithms avoid the time-consuming candidate generation stage and the need of scanning the original dataset multiple times for exact utility values. A novel tree structure, named tail-node tree (TN-tree) is proposed as a key element of our algorithms to maintain complete utililty-information of existing itemsets of a dataset. The performance of HUIL-TN and HUI-TN was evaluated against state-of-the-art reference methods on various datasets. Experimental results showed that our algorithms exceed or close to the best performance on all datasets in terms of running time, while other algorithms can only excel in certain types of dataset. Scalability tests were also performed and our algorithms obtained the flattest curves among all competitors.

show abstract

“…Several variants of mining high utility itemsets were also proposed. Vo et al presented the MCH-Miner to find high utility itemsets with varying unit profits of items in a dataset by parallel processing [32]. It was assumed that the profit values of all items might change along with item promotion, supply chain cost, or other factors.…”

Section: B Utility Miningmentioning

confidence: 99%

Using Tree Structure to Mine High Temporal Fuzzy Utility Itemsets

Hong

Huang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Data mining is a critical technology for extracting valuable knowledge from databases. It has been used in many fields, like retail, finance, biology, etc. In computational intelligence, fuzzy logic has been applied in many intelligent systems widely because it is simple and similar to human inference. Fuzzy utility mining combines utility mining and fuzzy logic for getting linguistic utility knowledge. In this paper, we study a more challenging, complicated, but practical topic called temporal fuzzy utility data mining, which considers the temporal periods in transactions, purchased amounts, item profits, and understandable linguistic terms as important factors. Although an Apriori-based algorithm was proposed previously, its execution was not efficient. We thus use a modified tree structure based on the classical frequent-pattern tree to improve its performance. A tree-based mining algorithm is also proposed to mine temporal fuzzy utility itemsets from quantitative transactional databases. The tree structure is built to keep all temporal fuzzy utility 1-itemsets in a database. All the high temporal fuzzy utility itemsets in a database can be obtained by traversing the treebased structure. The proposed algorithm gets the final results through two phases. In the first phase, a procedure like FP-Growth is used to find the candidate itemsets. In the second phase, the temporal fuzzy utility database is scanned to decide whether the candidate itemsets are desired. Experimental results show that the proposed algorithm is superior to the existing algorithm for temporal fuzzy utility mining in terms of processing time and used memory.INDEX TERMS fuzzy set, quantitative database, temporal fuzzy utility mining, tree structure, utility mining.

show abstract

A Multi-Core Approach to Efficiently Mining High-Utility Itemsets in Dynamic Profit Databases

Cited by 28 publications

References 46 publications

Parallel Algorithm to Efficiently Mine High Utility Itemset

Parallel Algorithm to Efficiently Mine High Utility Itemset

HUIL-TN & HUI-TN: Mining high utility itemsets based on pattern-growth

Using Tree Structure to Mine High Temporal Fuzzy Utility Itemsets

Contact Info

Product

Resources

About