On the connection of hypergraph theory with formal concept analysis and rough set theory

被引：36

作者：

Cattaneo, Gianpiero ^{[1
]}

Chiaselotti, Giampiero ^{[2
]}

Ciucci, Davide ^{[1
]}

Gentile, Tommaso ^{[2
]}

机构：

[1] Univ Milano Bicocca, Dept Informat Syst & Commun, I-20126 Milan, Italy

[2] Univ Calabria, Dept Math & Informat, I-87036 Arcavacata Di Rende, CS, Italy

来源：

INFORMATION SCIENCES | 2016年 / 330卷

关键词：

Rough sets; Formal concept analysis; Hypergraphs; CO-ENTROPY; DEPENDENCIES; PARTITIONS; COVERINGS;

D O I：

10.1016/j.ins.2015.09.054

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We present a unique framework for connecting different topics: hypergraphs from one side and Formal Concept Analysis and Rough Set Theory from the other. This is done through the formal equivalence among Boolean information tables, formal contexts and hypergraphs. Links with generic (i.e., not Boolean) information tables are established, through so-called nominal scaling. The particular case of k-uniform complete hypergraphs will then be studied. In this framework, we are able to solve typical problems of Rough Set Theory and Formal Concept Analysis using combinatorial techniques. More in detail, we will give a formula to compute the degree of dependency and the partial implication between two sets of attributes, compute the set of reducts and define the structure of the partitions generated by all the definable indiscernibility relations. (C) 2015 Elsevier Inc. All rights reserved.

引用

页码：342 / 357

页数：16

共 50 条

[31] Review on Application of Rough Set Theory
Yan, Hua
MANUFACTURING PROCESS AND EQUIPMENT, PTS 1-4, 2013, 694-697 : 2905 - 2909
[32] α-RST:: a generalization of rough set theory
Quafafou, M
INFORMATION SCIENCES, 2000, 124 (1-4) : 301 - 316
[33] Formal Concept Analysis in relational database and rough relational database
Jiang, Feng
Sui, Yuefei
Cao, Cungen
FUNDAMENTA INFORMATICAE, 2007, 80 (04) : 435 - 451
[34] A knowledge elicitation framework in ranking healthcare providers using rough set with formal concept analysis
Mohapatro, Arati
Mahendran, S. K.
Das, Tapan Kumar
INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING, 2020, 23 (04) : 396 - 407
[35] Rough-set concept analysis: Interpreting RS-definable concepts based on ideas from formal concept analysis
Yao, Yiyu
INFORMATION SCIENCES, 2016, 346 : 442 - 462
[36] Partitions, Coverings, Reducts and Rule Learning in Rough Set Theory
Yao, Yiyu
Fu, Rong
ROUGH SETS AND KNOWLEDGE TECHNOLOGY, 2011, 6954 : 101 - 109
[37] Application of the rough set theory to the analysis of food safety in fish processing
Wasikowska, Barbara
Linowska, Angelika A.
KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS (KSE 2021), 2021, 192 : 3342 - 3350
[38] Gene selection using rough set theory
Li, Dingfang
Zhang, Wen
ROUGH SETS AND KNOWLEDGE TECHNOLOGY, PROCEEDINGS, 2006, 4062 : 778 - 785
[39] On Uncertainty Measure Issues in Rough Set Theory
Tang, Jianguo
Wang, Jianghua
Wu, Chunling
Ou, Guojian
IEEE ACCESS, 2020, 8 : 91089 - 91102
[40] A survey on rough set theory and its applications
Zhang, Qinghua
Xie, Qin
Wang, Guoyin
CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY, 2016, 1 (04) : 323 - 333

← 1 2 3 4 5 →