Distributed Scalable Association Rule Mining over Covid-19 Data (2024)

@inproceedings{5830cd907a2449fbb0212ceb1c3f8e54,

title = "Distributed Scalable Association Rule Mining over Covid-19 Data",

keywords = "Apriori, Association rule mining, Big data, FP-growth, Machine learning, Spark",

author = "Mahtab Shahin and Wissem Inoubli and Shah, {Syed Attique} and Yahia, {Sadok Ben} and Dirk Draheim",

note = "Funding Information: Acknowledgements. This work has been conducted in the project “ICT programme” which was supported by the European Union through the European Social Fund. Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 8th International Conference on Future Data and Security Engineering , FDSE 2021 ; Conference date: 24-11-2021 Through 26-11-2021",

year = "2021",

doi = "10.1007/978-3-030-91387-8_3",

language = "English",

isbn = "9783030913861",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "39--52",

editor = "Dang, {Tran Khanh} and Josef K{\"u}ng and Chung, {Tai M.} and Makoto Takizawa",

booktitle = "Future Data and Security Engineering - 8th International Conference, FDSE 2021, Proceedings",

}

Distributed Scalable Association Rule Mining over Covid-19 Data. / Shahin, Mahtab; Inoubli, Wissem; Shah, Syed Attique et al.
Future Data and Security Engineering - 8th International Conference, FDSE 2021, Proceedings. ed. / Tran Khanh Dang; Josef Küng; Tai M. Chung; Makoto Takizawa. Springer Science and Business Media Deutschland GmbH, 2021. p. 39-52 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13076 LNCS).

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

TY - GEN

T1 - Distributed Scalable Association Rule Mining over Covid-19 Data

AU - Shahin, Mahtab

AU - Inoubli, Wissem

AU - Shah, Syed Attique

AU - Yahia, Sadok Ben

AU - Draheim, Dirk

N1 - Funding Information:Acknowledgements. This work has been conducted in the project “ICT programme” which was supported by the European Union through the European Social Fund.Publisher Copyright:© 2021, Springer Nature Switzerland AG.

PY - 2021

Y1 - 2021

KW - Apriori

KW - Association rule mining

KW - Big data

KW - FP-growth

KW - Machine learning

KW - Spark

UR - http://www.scopus.com/inward/record.url?scp=85121143505&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85121143505&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-91387-8_3

DO - 10.1007/978-3-030-91387-8_3

M3 - Conference contribution

AN - SCOPUS:85121143505

SN - 9783030913861

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 39

EP - 52

BT - Future Data and Security Engineering - 8th International Conference, FDSE 2021, Proceedings

A2 - Dang, Tran Khanh

A2 - Küng, Josef

A2 - Chung, Tai M.

A2 - Takizawa, Makoto

PB - Springer Science and Business Media Deutschland GmbH

T2 - 8th International Conference on Future Data and Security Engineering , FDSE 2021

Y2 - 24 November 2021 through 26 November 2021

ER -