DocumentCode
226930
Title
Parallel mining of fuzzy association rules on dense data sets
Author
Burda, Michal ; Pavliska, Viktor ; Valasek, Radek
Author_Institution
Inst. for Res. & Applic. of Fuzzy Modeling, Univ. of Ostrava, Ostrava, Czech Republic
fYear
2014
fDate
6-11 July 2014
Firstpage
2156
Lastpage
2162
Abstract
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 [1]. Having adopted the master/slave architecture, we propose a simple recursion threshold technique to allow load-balancing for high scalability.
Keywords
data mining; fuzzy set theory; parallel algorithms; resource allocation; search problems; OPUS search algorithm; dense data sets; fuzzy association rules mining; load-balancing; master/slave architecture; parallel mining; scalable parallel algorithm; Association rules; Bismuth; Fuzzy sets; Parallel algorithms; Pragmatics; Scalability;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on
Conference_Location
Beijing
Print_ISBN
978-1-4799-2073-0
Type
conf
DOI
10.1109/FUZZ-IEEE.2014.6891780
Filename
6891780
Link To Document