Mining Multiple Fuzzy Frequent Patterns with Compressed List Structures

被引：9

作者：

Lin, Jerry Chun-Wei ^{[1
]}

Wu, Jimmy Ming-Tai ^{[2
]}

Djenouri, Youcef ^{[3
]}

Srivastava, Gautam ^{[4
]}

Hong, Tzung-Pei ^{[5
]}

机构：

[1] Western Norway Univ Appl Sci, Bergen, Norway

[2] Shandong Univ Sci & Technol, Qingdao, Shandong, Peoples R China

[3] SINTEF Digital Math & Cybernet, Oslo, Norway

[4] Brandon Univ, Brandon, MB, Canada

[5] Natl Univ Kaohsiung, Kaohsiung, Taiwan

来源：

2020 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) | 2020年

关键词：

fuzzy-set theory; fuzzy data mining; fuzzy-list structure; pruning strategies; ALGORITHM; TREE;

D O I：

10.1109/fuzz48607.2020.9177543

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Fuzzy-set theory was invented to represent more meaningful representations of knowledge for human reasoning, which can also be applied and utilized for handling the quantitative database. In this paper, an efficient fuzzy mining (EFM) algorithm is presented to fast discover the multiple fuzzy frequent patterns from quantitative databases under type-2 fuzzy-set theory. A compressed fuzzy-list (CFL)-structure is developed to maintain complete information for rule generation. Two pruning techniques are developed to reduce the search space and speed up mining progress. Several experiments are carried out for the purpose of verifying the efficiency and effectiveness of the designed approach in terms of runtime and the number of examined nodes under different minimum support thresholds and the results indicated the designed EFM achieves the best performance compared to the existing models.

引用

页数：8

共 40 条

[1]

Agrawal R., 1993, SIGMOD Record, V22, P207, DOI 10.1145/170036.170072

[2]

Agrawal R., 1994, P 20 INT C, P487, DOI DOI 10.1007/BF02948845

[3]

Au WH, 1998, 1998 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AT THE IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE - PROCEEDINGS, VOL 1-2, P1314, DOI 10.1109/FUZZY.1998.686309

[4]

Chan Man Kuok, 1998, SIGMOD Record, V27, P41, DOI 10.1145/273244.273257

[5] Fuzzy Association Rule Mining with Type-2 Membership Functions [J].

Chen, Chun-Hao ;

Hong, Tzung-Pei ;

Li, Yu .

INTELLIGENT INFORMATION AND DATABASE SYSTEMS, PT II, 2015, 9012 :128-134

[6] Data mining: An overview from a database perspective [J].

Chen, MS ;

Han, JW ;

Yu, PS .

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 1996, 8 (06) :866-883

[7] Cancer incidence and mortality in China, 2014 [J].

Chen, Wanqing ;

Sun, Kexin ;

Zheng, Rongshou ;

Zeng, Hongmei ;

Zhang, Siwei ;

Xia, Changfa ;

Yang, Zhixun ;

Li, He ;

Zou, Xiaonong ;

He, Jie .

CHINESE JOURNAL OF CANCER RESEARCH, 2018, 30 (01) :1-12

[8]

Chung-I Chang, 2012, 2012 International Conference on Systems and Informatics (ICSAI 2012), P2294, DOI 10.1109/ICSAI.2012.6223511

[9]

Fournier-Viger P., 2017, Data Science and Pattern Recognition, V1, P54

[10] The SPMF Open-Source Data Mining Library Version 2 [J].

Fournier-Viger, Philippe ;

Lin, Jerry Chun-Wei ;

Gomariz, Antonio ;

Gueniche, Ted ;

Soltani, Azadeh ;

Deng, Zhihong ;

Hoang Thanh Lam .

MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2016, PT III, 2016, 9853 :36-40

← 1 2 3 4 →