A parallel algorithm to generate formal concepts for large data

Huaiguo Fu; Engelbert Mephu Nguifo

doi:10.1007/978-3-540-24651-0_33

A parallel algorithm to generate formal concepts for large data

Huaiguo Fu, Engelbert Mephu Nguifo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

34 Citations (Scopus)

Abstract

One of the most effective methods to deal with large data for data analysis and data mining is to develop parallel algorithm. Although Formal concept analysis is an effective tool for data analysis and knowledge discovery, it's very hard for concept lattice structures to face the complexity of very large data. So we propose a new parallel algorithm based on the NextClosure algorithm to generate formal concepts for large data.

Original language	English
Title of host publication	Concept Lattices
Editors	Peter Eklund
Publisher	Springer
Pages	394-401
Number of pages	8
ISBN (Print)	3540210431, 9783540210436
DOIs	https://doi.org/10.1007/978-3-540-24651-0_33
Publication status	Published - 2004
Externally published	Yes
Event	2nd International Conference on Formal Concept Analysis, ICFCA 2004 - Sydney, Australia Duration: 23 Feb 2004 → 26 Feb 2004

Publication series

Name	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume	2961
ISSN (Print)	0302-9743

Conference

Conference	2nd International Conference on Formal Concept Analysis, ICFCA 2004
Country/Territory	Australia
City	Sydney
Period	23/02/2004 → 26/02/2004

Access to Document

10.1007/978-3-540-24651-0_33

Cite this

@inproceedings{5a031c2f449a4d8a919065ea655527ac,

title = "A parallel algorithm to generate formal concepts for large data",

abstract = "One of the most effective methods to deal with large data for data analysis and data mining is to develop parallel algorithm. Although Formal concept analysis is an effective tool for data analysis and knowledge discovery, it's very hard for concept lattice structures to face the complexity of very large data. So we propose a new parallel algorithm based on the NextClosure algorithm to generate formal concepts for large data.",

author = "Huaiguo Fu and {Mephu Nguifo}, Engelbert",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2004.; null ; Conference date: 23-02-2004 Through 26-02-2004",

year = "2004",

doi = "10.1007/978-3-540-24651-0_33",

language = "English",

isbn = "3540210431",

series = "Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)",

publisher = "Springer",

pages = "394--401",

editor = "Peter Eklund",

booktitle = "Concept Lattices",

address = "United States",

}

Fu, H & Mephu Nguifo, E 2004, A parallel algorithm to generate formal concepts for large data. in P Eklund (ed.), Concept Lattices. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), vol. 2961, Springer, pp. 394-401, 2nd International Conference on Formal Concept Analysis, ICFCA 2004, Sydney, Australia, 23/02/2004. https://doi.org/10.1007/978-3-540-24651-0_33

A parallel algorithm to generate formal concepts for large data. / Fu, Huaiguo; Mephu Nguifo, Engelbert.

Concept Lattices. ed. / Peter Eklund. Springer, 2004. p. 394-401 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 2961).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A parallel algorithm to generate formal concepts for large data

AU - Fu, Huaiguo

AU - Mephu Nguifo, Engelbert

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2004.

PY - 2004

Y1 - 2004

N2 - One of the most effective methods to deal with large data for data analysis and data mining is to develop parallel algorithm. Although Formal concept analysis is an effective tool for data analysis and knowledge discovery, it's very hard for concept lattice structures to face the complexity of very large data. So we propose a new parallel algorithm based on the NextClosure algorithm to generate formal concepts for large data.

AB - One of the most effective methods to deal with large data for data analysis and data mining is to develop parallel algorithm. Although Formal concept analysis is an effective tool for data analysis and knowledge discovery, it's very hard for concept lattice structures to face the complexity of very large data. So we propose a new parallel algorithm based on the NextClosure algorithm to generate formal concepts for large data.

UR - http://www.scopus.com/inward/record.url?scp=9444265910&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-24651-0_33

DO - 10.1007/978-3-540-24651-0_33

M3 - Conference contribution

AN - SCOPUS:9444265910

SN - 3540210431

SN - 9783540210436

T3 - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

SP - 394

EP - 401

BT - Concept Lattices

A2 - Eklund, Peter

PB - Springer

Y2 - 23 February 2004 through 26 February 2004

ER -

A parallel algorithm to generate formal concepts for large data

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this