An efficient utility-list based high-utility itemset mining algorithm

Cheng, Zaihe1,2; Fang, Wei2; Shen, Wei2; Lin, Jerry Chun-Wei3; Yuan, Bo4

2022-07-01

10.1007/s10489-022-03850-4

APPLIED INTELLIGENCE   影响因子和分区

0924-669X

1573-7497

High-utility itemset mining (HUIM) is an important task in data mining that can retrieve more meaningful and useful patterns for decision-making. One-phase HUIM algorithms based on the utility-list structure have been shown to be the most efficient as they can mine high-utility itemsets (HUIs) without generating candidates. However, storing itemset information for the utility-list is time-consuming and memory consuming. To address this problem, we propose an efficient simplified utility-list-based HUIM algorithm (HUIM-SU). In the proposed HUIM-SU algorithm, the simplified utility-list is proposed to obtain all HUIs effectively and reduce memory usage in the depth-first search process. Based on the the simplified utility-list, repeated pruning according to the transaction-weighted utilisation (TWU) reduces the number of items. In addition, a construction tree and compressed storage are introduced to further reduce the search space and the memory usage. The extension utility and itemset TWU are then proposed to be the upper bounds, which reduce the search space considerably. Extensive experimental results on dense and sparse datasets indicate that the proposed HUIM-SU algorithm is highly efficient in terms of the number of candidates, memory usage, and execution time.

Data mining Pattern mining High-utility itemset mining Simplified utility-list

SCI ; EI

英语

其他

National Key R&D Program of China["2017YFC1601000","2017YFC1601800"] ; National Natural Science foundation of China["62073155","62106088","61673194","61672263"]

Computer Science

Computer Science, Artificial Intelligence

WOS:000824373500003

SPRINGER

20222912386067

Decision making ; Digital storage

Data Storage, Equipment and Techniques:722.1 ; Data Processing and Image Processing:723.2 ; Management:912.2

ENGINEERING

2-s2.0-85134351417

Web of Science

1.Wuxi Inst Technol, Sch Internet Things, Gaolang Rd, Wuxi 214121, Jiangsu, Peoples R China
2.Jiangnan Univ, Jiangsu Prov Engn Lab Pattern Recognit & Computat, Lihu Ave, Wuxi 214122, Jiangsu, Peoples R China
3.Western Norway Univ Appl Sci, Dept Comp Sci Elect Engn & Math Sci, Bergen, Norway
4.Southern Univ Sci & Technol, Comp Sci & Engn Dept, Shenzhen, Peoples R China

Cheng, Zaihe,Fang, Wei,Shen, Wei,et al. An efficient utility-list based high-utility itemset mining algorithm[J]. APPLIED INTELLIGENCE,2022,53(6):6992-7006.

Cheng, Zaihe,Fang, Wei,Shen, Wei,Lin, Jerry Chun-Wei,&Yuan, Bo.(2022).An efficient utility-list based high-utility itemset mining algorithm.APPLIED INTELLIGENCE,53(6),6992-7006.

Cheng, Zaihe,et al."An efficient utility-list based high-utility itemset mining algorithm".APPLIED INTELLIGENCE 53.6(2022):6992-7006.