On Selection of Efficient Sequential Pattern Mining Algorithm
Based on Characteristics of Data

D 2022

On Selection of Efficient Sequential Pattern Mining Algorithm Based on Characteristics of Data

PESCHEL, Jakub, Michal BATKO a Pavel ZEZULA

Základní údaje

Originální název

On Selection of Efficient Sequential Pattern Mining Algorithm Based on Characteristics of Data

Autoři

PESCHEL, Jakub (203 Česká republika, garant, domácí), Michal BATKO (203 Česká republika, domácí) a Pavel ZEZULA (203 Česká republika, domácí)

Vydání

Neuveden, 2022 IEEE International Symposium on Multimedia (ISM), od s. 202-205, 4 s. 2022

Nakladatel

IEEE

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10200 1.2 Computer and information sciences

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Kód RIV

RIV/00216224:14330/22:00127166

Organizační jednotka

Fakulta informatiky

ISBN

978-1-6654-7173-2

DOI

http://dx.doi.org/10.1109/ISM55400.2022.00044

UT WoS

000964457800037

Klíčová slova anglicky

Sequential Pattern Mining; GSP; SPAM; Prefix-span

Štítky

DISA, firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 16. 8. 2023 13:26, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

Sequential pattern mining, which is one of the core tasks in data mining, allows to gain insight into datasets with complex sequential data. As the task is computationally intensive, there are many different approaches that are suitable for various types of data. We explore the possibility of optimising the analysis of sequences based on the characteristic (quickly obtainable) properties of the analysed data. In this paper, we propose five such characteristics and explore the efficiency of three algorithms that are representatives of the three main approaches to sequential pattern mining. We discovered that it is possible to save up to 21% of the search time compared to the best-performing representative. We trained a decision tree model with 87% accuracy of choosing the best algorithm for selected data based on these characteristics.

Návaznosti

EF16_019/0000822, projekt VaV

Název: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur

Citovat

PESCHEL, Jakub, Michal BATKO a Pavel ZEZULA. On Selection of Efficient Sequential Pattern Mining Algorithm Based on Characteristics of Data. Online. In 2022 IEEE International Symposium on Multimedia (ISM). Neuveden: IEEE, 2022, s. 202-205. ISBN 978-1-6654-7173-2. Dostupné z: https://dx.doi.org/10.1109/ISM55400.2022.00044.

@inproceedings{2232098,
   author = {Peschel, Jakub and Batko, Michal and Zezula, Pavel},
   address = {Neuveden},
   booktitle = {2022 IEEE International Symposium on Multimedia (ISM)},
   doi = {http://dx.doi.org/10.1109/ISM55400.2022.00044},
   keywords = {Sequential Pattern Mining; GSP; SPAM; Prefix-span},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Neuveden},
   isbn = {978-1-6654-7173-2},
   pages = {202-205},
   publisher = {IEEE},
   title = {On Selection of Efficient Sequential Pattern Mining Algorithm Based on Characteristics of Data},
   url = {https://ieeexplore.ieee.org/abstract/document/10019622},
   year = {2022}
}

TY  - JOUR
ID  - 2232098
AU  - Peschel, Jakub - Batko, Michal - Zezula, Pavel
PY  - 2022
TI  - On Selection of Efficient Sequential Pattern Mining Algorithm Based on Characteristics of Data
PB  - IEEE
CY  - Neuveden
SN  - 9781665471732
KW  - Sequential Pattern Mining
KW  - GSP
KW  - SPAM
KW  - Prefix-span
UR  - https://ieeexplore.ieee.org/abstract/document/10019622
N2  - Sequential pattern mining, which is one of the core tasks in data mining, allows to gain insight into datasets with complex sequential data. As the task is computationally intensive, there are many different approaches that are suitable for various types of data. We explore the possibility of optimising the analysis of sequences based on the characteristic (quickly obtainable) properties of the analysed data. In this paper, we propose five such characteristics and explore the efficiency of three algorithms that are representatives of the three main approaches to sequential pattern mining. We discovered that it is possible to save up to 21% of the search time compared to the best-performing representative. We trained a decision tree model with 87% accuracy of choosing the best algorithm for selected data based on these characteristics.
ER  -

PESCHEL, Jakub, Michal BATKO a Pavel ZEZULA. On Selection of Efficient Sequential Pattern Mining Algorithm Based on Characteristics of Data. Online. In \textit{2022 IEEE International Symposium on Multimedia (ISM)}. Neuveden: IEEE, 2022, s.~202-205. ISBN~978-1-6654-7173-2. Dostupné z: https://dx.doi.org/10.1109/ISM55400.2022.00044.

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