Parallel Mining of Fuzzy Association Rules on Dense Data Sets
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F14%3AA1501BDK" target="_blank" >RIV/61988987:17610/14:A1501BDK - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Parallel Mining of Fuzzy Association Rules on Dense Data Sets
Original language description
The aim of this paper is to present a scalable parallel algorithm for fuzzy association rules mining that is suitable for dense data sets. Unlike most of other approaches, we have based the algorithm on the Webb's OPUS search algorithm. Having adopted the master/slave architecture, we propose a simple recursion threshold technique to allow load-balancing for high scalability.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: IT4Innovations Centre of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IEEE International Conference on Fuzzy Systems
ISBN
978-1-4799-2072-3
ISSN
1098-7584
e-ISSN
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Number of pages
7
Pages from-to
2156-2162
Publisher name
IEEE
Place of publication
Beijing, China
Event location
Beijing, China
Event date
Jul 6, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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