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@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.
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