Hadoop Based Mining Of Distributed Association Rules From Big Data

Data analysis techniques need to be improved to allow the processing of data. One of the most commonly used techniques is the Association Rule Mining (ARM). These rules are used to detect facts that often occur together within a dataset. Though several methods have been suggested for the extraction of association rules, problems arise when data becomes large. To overcome such issue, we propose, in this paper, an efficient approach for ARM based on MapReduce framework, adapted for processing large volumes of data. Furthermore, because real-life databases lead to huge number of rules including many redundant rules, our algorithm propose to mine a compact set of rules with no loss of information. The results of experiments tested on large real world datasets highlight the relevance of mined data.