print

Distributed scalable association rule mining over Covid-19 data

author
Shahin, Mahtab
Inoubli, Wissem
Shah, Syed Attique
Ben Yahia, Sadok
Draheim, Dirk
statement of authorship
Mahtab Shahin, Wissem Inoubli, Syed Attique Shah, Sadok Ben Yahia, Dirk Draheim
source
Future Data and Security Engineering : 8th International Conference, FDSE 2021, Virtual Event, November 24–26, 2021 : proceedings
location of publication
Cham
publisher
Springer Nature Switzerland AG
year of publication
2021
pages
p. 39-52
series
Lecture notes in computer science ; 13076
conference name, date
8th International Conference, FDSE 2021, Virtual Event, November 24–26, 2021
url
https://doi.org/10.1007/978-3-030-91387-8_3
subject term
suurandmed
tehisõpe
koroonaviirus SARS-CoV-2
Scopus
Conference Proceedings at Scopus
Article at Scopus
kvartiil
Q3
category (general)
Computer science
Mathematics
category (sub)
Computer science. General computer science
Mathematics. Theoretical computer science
keyword
association rule mining
big data
FP-growth
sparks
apriori
machine learning (ML)
ISSN
0302-9743
ISBN
978-3-030-91386-1
notes
Bibliogr.: 32 ref
scientific publication
teaduspublikatsioon
classifier
3.1
TTÜ department
tarkvarateaduse instituut
language
inglise
Uurimisrühm
Data science group
Information systems group
Shahin, M., Inoubli, W., Shah, S. A., Ben Yahia, S., Draheim, D. Distributed scalable association rule mining over Covid-19 data // Future Data and Security Engineering : 8th International Conference, FDSE 2021, Virtual Event, November 24–26, 2021 : proceedings. Cham : Springer Nature Switzerland AG, 2021. p. 39-52. (Lecture notes in computer science ; 13076). https://doi.org/10.1007/978-3-030-91387-8_3