FIMSIM: Discovering Communities By Frequent Item-Set Mining and Similarity Search
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F21%3A00119128" target="_blank" >RIV/00216224:14330/21:00119128 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-89657-7_28" target="_blank" >http://dx.doi.org/10.1007/978-3-030-89657-7_28</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-89657-7_28" target="_blank" >10.1007/978-3-030-89657-7_28</a>
Alternative languages
Result language
angličtina
Original language name
FIMSIM: Discovering Communities By Frequent Item-Set Mining and Similarity Search
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
<a href="/en/project/GA19-02033S" target="_blank" >GA19-02033S: Searching, Mining, and Annotating Human Motion Streams</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
14th International Conference on Similarity Search and Applications (SISAP)
ISBN
9783030896560
ISSN
0302-9743
e-ISSN
—
Number of pages
12
Pages from-to
372-383
Publisher name
Springer International Publishing
Place of publication
Cham
Event location
Dortmund, Germany
Event date
Sep 29, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000722252200028