Decision Logic for Rough Set-based Interrelationship Mining

被引：0

作者：

Kudo, Yasuo ^{[1
]}

Murai, Tetsuya ^{[2
]}

机构：

[1] Muroran Inst Technol, Coll Informat & Syst, Mizumoto 27-1, Muroran, Hokkaido 0508585, Japan

[2] Hokkaido Univ, Grad Sch Informat Sci & Technol, Sapporo, Hokkaido 0600814, Japan

来源：

2013 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING (GRC) | 2013年

关键词：

decision logic; interrelationship mining; low and high order decision rules; rough set;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Rough set-based decision rule extraction is a useful tool for data mining, however, almost approaches of decision rule extraction are based on comparison between values of the same attribute. On the other hand, the authors have proposed a concept of interrelationship mining that enables us to extract characteristics based on comparison between values of different attributes. In this paper, we consider logical aspects of interrelationship mining by introducing decision logic for interrelationship mining.

引用

页码：172 / 177

页数：6

共 50 条

[31] A rough set-based method for dual hesitant fuzzy soft sets based on decision making
Zhang, Haidong
He, Yanping
[J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2018, 35 (03) : 3437 - 3450
[32] Partition for the rough set-based text classification
Bao, YG
Asai, D
Du, XY
Ishii, N
[J]. ADVANCES IN WEB-AGE INFORMATION MANAGEMENT, PROCEEDINGS, 2003, 2762 : 181 - 188
[33] A Rough Set-Based Method for Updating Decision Rules on Attribute Values' Coarsening and Refining
Chen, Hongmei
Li, Tianrui
Luo, Chuan
Horng, Shi-Jinn
Wang, Guoyin
[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2014, 26 (12) : 2886 - 2899
[34] On Rough Set-Based Modeling and with Application to Process Decision for Forming Plate by Line Heating
Feng, Zhi-qiang
Jiao, Zi-quan
Chen, Shan-ben
Han, Jun-feng
Han, Xiang-xi
Yang, Run-dang
Liu, Cun-gen
[J]. JOURNAL OF SHIP PRODUCTION AND DESIGN, 2019, 35 (03): : 289 - 296
[35] Non-hierarchical Clustering of Decision Tables toward Rough Set-Based Group Decision Aid
Inuiguchi, Masahiro
Enomoto, Ryuta
Kusunoki, Yoshifumi
[J]. MODELING DECISIONS FOR ARTIFICIAL INTELLIGENCE (MDAI), 2010, 6408 : 195 - 206
[36] Set-based analysis of logic programs and reactive logic programs
Podelski, A
[J]. LOGIC PROGRAMMING - PROCEEDINGS OF THE 1997 INTERNATIONAL SYMPOSIUM, 1997, : 35 - 36
[37] Set-Based Approach in Mining Sequential Patterns
Gao, Shang
Alhajj, Reda
Rokne, Jon
Guan, Jiwen
[J]. 2009 24TH INTERNATIONAL SYMPOSIUM ON COMPUTER AND INFORMATION SCIENCES, 2009, : 217 - +
[38] Fuzzy Rough Set-Based Unstructured Text Categorization
Bharadwaj, Aditya
Ramanna, Sheela
[J]. ADVANCES IN ARTIFICIAL INTELLIGENCE, CANADIAN AI 2017, 2017, 10233 : 335 - 340
[39] A Framework on Rough Set-Based Partitioning Attribute Selection
Herawan, Tutut
Deris, Mustafa Mat
[J]. EMERGING INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE, 2009, 5755 : 91 - 100
[40] Structural risk minimization of rough set-based classifier
Jinfu Liu
Mingliang Bai
Na Jiang
Daren Yu
[J]. Soft Computing, 2020, 24 : 2049 - 2066

← 1 2 3 4 5 →