Mining High-Utility Itemsets of Generalized Quantity with Pattern-Growth Structures

2020 ◽  
pp. 447-464
Author(s):  
Ming-Yen Lin ◽  
Tzer-Fu Tu ◽  
Sue-Chen Hsueh
Keyword(s):  
Pattern Growth ◽  
High Utility ◽  
High Utility Itemsets

scholarly journals Mining High Utility Itemsets Based on Pattern Growth without Candidate Generation

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 35
Author(s):  
Yiwei Liu ◽  
Le Wang ◽  
Lin Feng ◽  
Bo Jin
Keyword(s):  
State Of The Art ◽  
Research Topic ◽  
Performance Gap ◽  
Utility Values ◽  
Original Dataset ◽  
Pattern Growth ◽  
Active Research ◽  
High Utility ◽  
High Utility Itemsets ◽  
Mining Algorithms

Mining high utility itemsets (HUIs) has been an active research topic in data mining in recent years. Existing HUI mining algorithms typically take two steps: generating candidates and identifying utility values of these candidate itemsets. The performance of these algorithms depends on the efficiency of both steps, both of which are usually time-consuming. In this study, we propose an efficient pattern-growth based HUI mining algorithm, called tail-node tree-based high-utility itemset (TNT-HUI) mining. This algorithm avoids the time-consuming candidate generation step, as well as the need of scanning the original dataset multiple times for exact utility values, as supported by a novel tree structure, named the tail-node tree (TN-Tree). The performance of TNT-HUI was evaluated in comparison with state-of-the-art benchmark methods on different datasets. Experimental results showed that TNT-HUI outperformed benchmark algorithms in both execution time and memory use by orders of magnitude. The performance gap is larger for denser datasets and lower thresholds.

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

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248349
Author(s):  
Le Wang ◽  
Shui Wang
Keyword(s):  
Data Mining ◽  
State Of The Art ◽  
Research Topic ◽  
Running Time ◽  
Original Dataset ◽  
Pattern Growth ◽  
Active Research ◽  
High Utility ◽  
High Utility Itemsets ◽  
Mining Algorithms

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.

An efficient fast algorithm for discovering closed+ high utility itemsets

2016 ◽  
Vol 45 (1) ◽  
pp. 44-74 ◽  
Author(s):  
Jayakrushna Sahoo ◽  
Ashok Kumar Das ◽  
A. Goswami
Keyword(s):  
Fast Algorithm ◽  
High Utility ◽  
High Utility Itemsets

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

2021 ◽  
Vol 186 ◽  
pp. 115741
Author(s):  
Trinh D.D. Nguyen ◽  
Loan T.T. Nguyen ◽  
Lung Vu ◽  
Bay Vo ◽  
Witold Pedrycz
Keyword(s):  
Efficient Algorithms ◽  
High Utility ◽  
High Utility Itemsets

MINING CORRELATED HIGH UTILITY ITEMSETS ON INCREMENTAL DATABASE

2021 ◽  
Author(s):  
Nguyen Van Le ◽  
Nguyen Thi Thanh Thuy ◽  
Manh Thien Ly
Keyword(s):  
High Utility ◽  
High Utility Itemsets
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