الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Generation of data is currently on a rapid rise. The need for fast processing of this data is urgent for various organisations. Distributed Data Mining of Association Rules is one of the most important types of knowledge to be extracted from data. The purpose of this thesis is to enhance the Distributed Data Mining of Association Rules process in terms of speed and computational cost. The current applications of Apriori algorithm on Apache Hadoop are usually done by performing a series of MapReduce Jobs. The execution of multiple jobs requires time and the communication cost per job is high.