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.

Basic information

Original name

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

Authors

PESCHEL, Jakub (203 Czech Republic, guarantor, belonging to the institution), Michal BATKO (203 Czech Republic, belonging to the institution), Jakub VALČÍK (203 Czech Republic), Jan SEDMIDUBSKÝ (203 Czech Republic, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, belonging to the institution)

Edition

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

Publisher

Springer International Publishing

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10200 1.2 Computer and information sciences

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

References:

URL

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/21:00119128

Organization unit

Faculty of Informatics

ISBN

978-3-030-89656-0

ISSN

DOI

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

UT WoS

000722252200028

Keywords in English

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

Abstract

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.

Links

GA19-02033S, research and development project

Name: Vyhledávání, analytika a anotace datových toků lidských pohybů

Investor: Czech Science Foundation

Citovat

PESCHEL, Jakub, Michal BATKO, Jakub VALČÍK, Jan SEDMIDUBSKÝ and 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, p. 372-383. ISBN 978-3-030-89656-0. Available from: 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 and 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, p.~372-383. ISBN~978-3-030-89656-0. Available from: https://dx.doi.org/10.1007/978-3-030-89657-7\_{}28.

Detailed Information on Publication Record