GC and other methods for full and partial context coverage

Kristo Raun; Ants Torim; Sadok Ben Yahia

doi:10.1016/j.procs.2021.08.077

GC and other methods for full and partial context coverage

Kristo Raun
, Ants Torim^*
, Sadok Ben Yahia

^*Corresponding author for this work

Tallin University of Technology

Research output: Contribution to journal › Conference article › Research › peer-review

Abstract

Formal Concept Analysis (FCA) is a popular method for knowledge discovery and data mining in binary data. A shortcoming of FCA is the huge number of formal concepts that may be drawn from formal contexts (binary data tables) of moderate size or larger. A strategy to deal with this shortcoming is to extract a subset of formal concepts that cover the context either fully or partially. We compare different methods for generating full and partial concept cover, present a simple Greedy Coverage (GC) method, and show that it is an efficient option, especially for generating partial concept cover.

Original language	English
Journal	Procedia Computer Science
Volume	192
Pages (from-to)	746-755
ISSN	1877-0509
DOIs	https://doi.org/10.1016/j.procs.2021.08.077
Publication status	Published - 2021
Externally published	Yes
Event	25th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2021 - Szczecin, Poland Duration: 8. Sept 2021 → 10. Sept 2021

Conference

Conference	25th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2021
Country/Territory	Poland
City	Szczecin
Period	08/09/2021 → 10/09/2021

Keywords

Data analysis and pattern recognition
Formal concept analysis
Knowledge discovery and data mining
Knowledge representation
Management

Documents & Links

10.1016/j.procs.2021.08.077Licence: CC BY-NC-ND

SDU Link

Check access to the material in SDU Library's search engine

Cite this

@inproceedings{9c9f917379ff4d6f9b28878954c43438,

title = "GC and other methods for full and partial context coverage",

abstract = "Formal Concept Analysis (FCA) is a popular method for knowledge discovery and data mining in binary data. A shortcoming of FCA is the huge number of formal concepts that may be drawn from formal contexts (binary data tables) of moderate size or larger. A strategy to deal with this shortcoming is to extract a subset of formal concepts that cover the context either fully or partially. We compare different methods for generating full and partial concept cover, present a simple Greedy Coverage (GC) method, and show that it is an efficient option, especially for generating partial concept cover.",

keywords = "Data analysis and pattern recognition, Formal concept analysis, Knowledge discovery and data mining, Knowledge representation, Management",

author = "Kristo Raun and Ants Torim and Yahia, \{Sadok Ben\}",

note = "Publisher Copyright: {\textcopyright} 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International.; 25th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2021 ; Conference date: 08-09-2021 Through 10-09-2021",

year = "2021",

doi = "10.1016/j.procs.2021.08.077",

language = "English",

volume = "192",

pages = "746--755",

journal = "Procedia Computer Science",

issn = "1877-0509",

publisher = "Elsevier",

}

TY - GEN

T1 - GC and other methods for full and partial context coverage

AU - Raun, Kristo

AU - Torim, Ants

AU - Yahia, Sadok Ben

N1 - Publisher Copyright: © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International.

PY - 2021

Y1 - 2021

N2 - Formal Concept Analysis (FCA) is a popular method for knowledge discovery and data mining in binary data. A shortcoming of FCA is the huge number of formal concepts that may be drawn from formal contexts (binary data tables) of moderate size or larger. A strategy to deal with this shortcoming is to extract a subset of formal concepts that cover the context either fully or partially. We compare different methods for generating full and partial concept cover, present a simple Greedy Coverage (GC) method, and show that it is an efficient option, especially for generating partial concept cover.

AB - Formal Concept Analysis (FCA) is a popular method for knowledge discovery and data mining in binary data. A shortcoming of FCA is the huge number of formal concepts that may be drawn from formal contexts (binary data tables) of moderate size or larger. A strategy to deal with this shortcoming is to extract a subset of formal concepts that cover the context either fully or partially. We compare different methods for generating full and partial concept cover, present a simple Greedy Coverage (GC) method, and show that it is an efficient option, especially for generating partial concept cover.

KW - Data analysis and pattern recognition

KW - Formal concept analysis

KW - Knowledge discovery and data mining

KW - Knowledge representation

KW - Management

U2 - 10.1016/j.procs.2021.08.077

DO - 10.1016/j.procs.2021.08.077

M3 - Conference article

AN - SCOPUS:85116886662

SN - 1877-0509

VL - 192

SP - 746

EP - 755

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 25th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2021

Y2 - 8 September 2021 through 10 September 2021

ER -

GC and other methods for full and partial context coverage

Abstract

Conference

Keywords

Documents & Links

SDU Link

Fingerprint

Cite this