Abstract
Visual analytics uses interactive visualizations in order to incorporate user’s knowledge and cognitive capability into data analytics processes. The progressive visual analytic paradigm simplifies the analytic process when it comes to large datasets. It uses the interactive sequential pattern mining algorithm which reports patterns as it finds them. But, the sequential pattern mining algorithms like SPAM, SPADE and PrefixSpan are suited for a single-node environment only. It is also constrained by the available size of memory and computational power while handling a very large quantity of data. So to overcome these challenges, the proposed MapReduce frequent pattern mining (MR-FPM) algorithm distributes data across various nodes in the Hadoop cluster, finds the candidate itemsets and counts their support using the MapReduce paradigm. The patterns with supportless than the user-defined minsup are discarded. Experimental results show that MR-FPM continuously outperforms SPAM when the minsup is decreased.
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