FIMSIM: Discovering Communities By Frequent Item-Set Mining and
Similarity Search

D 2021

FIMSIM: Discovering Communities By Frequent Item-Set Mining and Similarity Search

PESCHEL, Jakub, Michal BATKO, Jakub VALČÍK, Jan SEDMIDUBSKÝ, Pavel ZEZULA et. al.

Základní údaje

Originální název

FIMSIM: Discovering Communities By Frequent Item-Set Mining and Similarity Search

Autoři

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

Vydání

Cham, 14th International Conference on Similarity Search and Applications (SISAP), od s. 372-383, 12 s. 2021

Nakladatel

Springer International Publishing

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

tištěná verze "print"

Odkazy

URL

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/21:00119128

Organizační jednotka

Fakulta informatiky

ISBN

978-3-030-89656-0

ISSN

DOI

http://dx.doi.org/10.1007/978-3-030-89657-7_28

UT WoS

000722252200028

Klíčová slova anglicky

community mining;frequent item-set mining;similarity search;network analysis

Štítky

core_B, firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 28. 4. 2022 09:57, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

With the growth of structured graph data, the analysis of networks is an important topic. Community mining is one of the main analytical tasks of network analysis. Communities are dense clusters of nodes, possibly containing additional information about a network. In this paper, we present a community-detection approach, called FIMSIM, which is based on principles of frequent item-set mining and similarity search. The frequent item-set mining is used to extract cores of the communities, and a proposed similarity function is applied to discover suitable surroundings of the cores. The proposed approach outperforms the state-of-the-art DB-Link Clustering algorithm while enabling the easier selection of parameters. In addition, possible modifications are proposed to control the resulting communities better.

Návaznosti

GA19-02033S, projekt VaV

Název: Vyhledávání, analytika a anotace datových toků lidských pohybů

Investor: Grantová agentura ČR, Searching, Mining, and Annotating Human Motion Streams

Citovat

PESCHEL, Jakub, Michal BATKO, Jakub VALČÍK, Jan SEDMIDUBSKÝ a Pavel ZEZULA. FIMSIM: Discovering Communities By Frequent Item-Set Mining and Similarity Search. In 14th International Conference on Similarity Search and Applications (SISAP). Cham: Springer International Publishing, 2021, s. 372-383. ISBN 978-3-030-89656-0. Dostupné z: https://dx.doi.org/10.1007/978-3-030-89657-7_28.

@inproceedings{1785857,
   author = {Peschel, Jakub and Batko, Michal and Valčík, Jakub and Sedmidubský, Jan and Zezula, Pavel},
   address = {Cham},
   booktitle = {14th International Conference on Similarity Search and Applications (SISAP)},
   doi = {http://dx.doi.org/10.1007/978-3-030-89657-7_28},
   keywords = {community mining;frequent item-set mining;similarity search;network analysis},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Cham},
   isbn = {978-3-030-89656-0},
   pages = {372-383},
   publisher = {Springer International Publishing},
   title = {FIMSIM: Discovering Communities By Frequent Item-Set Mining and Similarity Search},
   url = {https://link.springer.com/chapter/10.1007%2F978-3-030-89657-7_28},
   year = {2021}
}

TY  - JOUR
ID  - 1785857
AU  - Peschel, Jakub - Batko, Michal - Valčík, Jakub - Sedmidubský, Jan - Zezula, Pavel
PY  - 2021
TI  - FIMSIM: Discovering Communities By Frequent Item-Set Mining and Similarity Search
PB  - Springer International Publishing
CY  - Cham
SN  - 9783030896560
KW  - community mining;frequent item-set mining;similarity search;network analysis
UR  - https://link.springer.com/chapter/10.1007%2F978-3-030-89657-7_28
N2  - With the growth of structured graph data, the analysis of networks is an important topic. Community mining is one of the main analytical tasks of network analysis. Communities are dense clusters of nodes, possibly containing additional information about a network. In this paper, we present a community-detection approach, called FIMSIM, which is based on principles of frequent item-set mining and similarity search. The frequent item-set mining is used to extract cores of the communities, and a proposed similarity function is applied to discover suitable surroundings of the cores. The proposed approach outperforms the state-of-the-art DB-Link Clustering algorithm while enabling the easier selection of parameters. In addition, possible modifications are proposed to control the resulting communities better.
ER  -

PESCHEL, Jakub, Michal BATKO, Jakub VALČÍK, Jan SEDMIDUBSKÝ a Pavel ZEZULA. FIMSIM: Discovering Communities By Frequent Item-Set Mining and Similarity Search. In \textit{14th International Conference on Similarity Search and Applications (SISAP)}. Cham: Springer International Publishing, 2021, s.~372-383. ISBN~978-3-030-89656-0. Dostupné z: https://dx.doi.org/10.1007/978-3-030-89657-7\_{}28.

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