Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (6): 1676-1686.DOI: 10.11772/j.issn.1001-9081.2022060865
Special Issue: 综述; CCF第37届中国计算机应用大会 (CCF NCCA 2022)
• The 37 CCF National Conference of Computer Applications (CCF NCCA 2022) • Previous Articles Next Articles
Zhihui GAO, Meng HAN(), Shujuan LIU, Ang LI, Dongliang MU
Received:
2022-06-16
Revised:
2022-07-15
Accepted:
2022-07-27
Online:
2022-08-15
Published:
2023-06-10
Contact:
Meng HAN
About author:
GAO Zhihui, born in 1996, M. S. candidate. Her research interests include big data mining.Supported by:
通讯作者:
韩萌
作者简介:
高智慧(1996—),女,山东临沂人,硕士研究生,主要研究方向:大数据挖掘基金资助:
CLC Number:
Zhihui GAO, Meng HAN, Shujuan LIU, Ang LI, Dongliang MU. Survey of high utility itemset mining methods based on intelligent optimization algorithm[J]. Journal of Computer Applications, 2023, 43(6): 1676-1686.
高智慧, 韩萌, 刘淑娟, 李昂, 穆栋梁. 基于智能优化算法的高效用项集挖掘方法综述[J]. 《计算机应用》唯一官方网站, 2023, 43(6): 1676-1686.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022060865
项 | 外部效用 | 事务加权效用 |
---|---|---|
a | 9 | 266 |
b | 7 | 262 |
c | 6 | 342 |
d | 3 | 336 |
e | 1 | 195 |
Tab. 1 Utility list
项 | 外部效用 | 事务加权效用 |
---|---|---|
a | 9 | 266 |
b | 7 | 262 |
c | 6 | 342 |
d | 3 | 336 |
e | 1 | 195 |
标识符 | 事务 | 各项对应的数量 | 事务效用 |
---|---|---|---|
T1 | {a,b,d} | {5,3,1} | 67 |
T2 | {b,c,d,e} | {7,2,6,1} | 70 |
T3 | {a,b,c,d,e} | {9,3,2,5,2} | 125 |
T4 | {b,c} | {7,4} | 73 |
T5 | {a,c,d} | {4,5,8} | 74 |
Tab. 2 Transaction list
标识符 | 事务 | 各项对应的数量 | 事务效用 |
---|---|---|---|
T1 | {a,b,d} | {5,3,1} | 67 |
T2 | {b,c,d,e} | {7,2,6,1} | 70 |
T3 | {a,b,c,d,e} | {9,3,2,5,2} | 125 |
T4 | {b,c} | {7,4} | 73 |
T5 | {a,c,d} | {4,5,8} | 74 |
数据集 | 事务数 | 项目数 | 事务平均长度 | 密度/% | 类型 |
---|---|---|---|---|---|
chess | 3 196 | 75 | 37.00 | 49.33 | 密集 |
connect | 67 557 | 129 | 43.00 | 33.33 | 密集 |
mushrooms | 8 416 | 119 | 23.00 | 19.33 | 密集 |
accidents | 340 183 | 468 | 33.80 | 7.22 | 密集 |
retail | 88 162 | 16 470 | 10.30 | 0.06 | 稀疏 |
foodmart | 1 112 949 | 46 086 | 7.23 | 0.02 | 稀疏 |
Tab. 3 Parameters of datasets
数据集 | 事务数 | 项目数 | 事务平均长度 | 密度/% | 类型 |
---|---|---|---|---|---|
chess | 3 196 | 75 | 37.00 | 49.33 | 密集 |
connect | 67 557 | 129 | 43.00 | 33.33 | 密集 |
mushrooms | 8 416 | 119 | 23.00 | 19.33 | 密集 |
accidents | 340 183 | 468 | 33.80 | 7.22 | 密集 |
retail | 88 162 | 16 470 | 10.30 | 0.06 | 稀疏 |
foodmart | 1 112 949 | 46 086 | 7.23 | 0.02 | 稀疏 |
算法名称 | 相应 算法 | 更新 策略 | 剪枝策略 | 对比算法 | 数据集 | 参数 设置 | 优缺点 |
---|---|---|---|---|---|---|---|
WARM SWARM | 传统 更新 策略 | 无 | Apriori | T5L10I10K、mushroom、 Dermatology、Adult、 Zoo、TeachingEval、 SoybeanLarge | ω=1 c1=c2=1 | 优点:挖掘速度快 缺点:进行权重拟合有一定的开销 | |
sigmoid 函数 | 无 | HUPEUMU-GRAM | chess、connect、 mushroom、accidents | ω=0.9 c1=c2=2 ND | |||
PSO | sigmoid 函数 | 有 | HUPEUMU-GRAM | chess、mushroom、 connect、accidents、 foodmart、retail | ω=0.9 c1=c2=2 ND | ||
HUIF-PSO | 贪心 | 无 | HUPEUMU-GRAM、 HUIM-BPSO、IHUP、 UP-Growth | chess、connect、 mushroom、 accidents_10% | ND | 优点:运行速度快 缺点:需要手动设置阈值, | |
轮盘赌 | 无 | HUIM-BPSO、 HUPE-GRAM | chess、mushroom、 retail、foodmart | ND | |||
轮盘赌 | 无 | HAUI-Miner、 EHAUPM | connect、chess、 accidents_10%、 T25N100D50K | ND | |||
轮盘赌 | 无 | Bio-HUIF-GA、 Bio-HUIF-PSO、 Bio-HUIF-BA | chess、mushroom、 foodmart、connect、 accident_10% | c1=c2=2 | |||
基于 集合 | 无 | HUIM-BPSOsig、 HUIM-BPSO | chess、 connect、 mushroom、 accidents_10% | ω=c1=c2=1 ND | |||
基于 路由图 | 有 | HUPEUMU-GRAM、 HUIM-BPSO、 HUI-Miner、EFIM | chess、connect、retail、 foodmart、mushroom、 accidents_10% | ND | |||
传统 更新 策略 | 有 | HURI | chess、mushroom、 retail、foodmart | ||||
有向图 | 无 | CHUD、 CHUI-Miner | chess、retail、 foodmart、BMS、 mushroom、 chainstore | ND | |||
位图 | 有 | HUPEUMU-GARM、 HUIM-BPSOsig、 HUIM-BPSO | chess、 connect、 mushroom、 accidents_10% | ND | |||
BA | 传统 更新 策略 | 无 | HUPEUMU-GRAM、 HUIM-BPSO、 IHUP、UP-Growth | chess、connect、 mushroom、 accidents_10% | ND | ||
BGWO | 布尔 运算 | 无 | UP-Growth、 HUPEUMU-GARM、 Bio-HUIF-BA、 Bio-HUIF-GA、 HUIM-BPSO | chess、 connect、 mushroom、 accidents | ND | ||
AF | 传统 更新 策略 | 无 | HUPEUMU-GARM、 HUIM-BPSOsig | chess、connect、 mushroom、 accidents_10% | ND |
Tab. 4 Summary of HUIM methods based on swarm intelligence optimization algorithms
算法名称 | 相应 算法 | 更新 策略 | 剪枝策略 | 对比算法 | 数据集 | 参数 设置 | 优缺点 |
---|---|---|---|---|---|---|---|
WARM SWARM | 传统 更新 策略 | 无 | Apriori | T5L10I10K、mushroom、 Dermatology、Adult、 Zoo、TeachingEval、 SoybeanLarge | ω=1 c1=c2=1 | 优点:挖掘速度快 缺点:进行权重拟合有一定的开销 | |
sigmoid 函数 | 无 | HUPEUMU-GRAM | chess、connect、 mushroom、accidents | ω=0.9 c1=c2=2 ND | |||
PSO | sigmoid 函数 | 有 | HUPEUMU-GRAM | chess、mushroom、 connect、accidents、 foodmart、retail | ω=0.9 c1=c2=2 ND | ||
HUIF-PSO | 贪心 | 无 | HUPEUMU-GRAM、 HUIM-BPSO、IHUP、 UP-Growth | chess、connect、 mushroom、 accidents_10% | ND | 优点:运行速度快 缺点:需要手动设置阈值, | |
轮盘赌 | 无 | HUIM-BPSO、 HUPE-GRAM | chess、mushroom、 retail、foodmart | ND | |||
轮盘赌 | 无 | HAUI-Miner、 EHAUPM | connect、chess、 accidents_10%、 T25N100D50K | ND | |||
轮盘赌 | 无 | Bio-HUIF-GA、 Bio-HUIF-PSO、 Bio-HUIF-BA | chess、mushroom、 foodmart、connect、 accident_10% | c1=c2=2 | |||
基于 集合 | 无 | HUIM-BPSOsig、 HUIM-BPSO | chess、 connect、 mushroom、 accidents_10% | ω=c1=c2=1 ND | |||
基于 路由图 | 有 | HUPEUMU-GRAM、 HUIM-BPSO、 HUI-Miner、EFIM | chess、connect、retail、 foodmart、mushroom、 accidents_10% | ND | |||
传统 更新 策略 | 有 | HURI | chess、mushroom、 retail、foodmart | ||||
有向图 | 无 | CHUD、 CHUI-Miner | chess、retail、 foodmart、BMS、 mushroom、 chainstore | ND | |||
位图 | 有 | HUPEUMU-GARM、 HUIM-BPSOsig、 HUIM-BPSO | chess、 connect、 mushroom、 accidents_10% | ND | |||
BA | 传统 更新 策略 | 无 | HUPEUMU-GRAM、 HUIM-BPSO、 IHUP、UP-Growth | chess、connect、 mushroom、 accidents_10% | ND | ||
BGWO | 布尔 运算 | 无 | UP-Growth、 HUPEUMU-GARM、 Bio-HUIF-BA、 Bio-HUIF-GA、 HUIM-BPSO | chess、 connect、 mushroom、 accidents | ND | ||
AF | 传统 更新 策略 | 无 | HUPEUMU-GARM、 HUIM-BPSOsig | chess、connect、 mushroom、 accidents_10% | ND |
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