Parallel mining of fuzzy association rules on dense data sets
Michal Burda, Viktor Pavliska, Radek Valášek · 2014
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.