A Parallel MapReduce Algorithm to Efficiently Support Itemset Mining on High Dimensional Data

In today's world, large volumes of data are being continuously generated by many scientific applications, such as bioinformatics or networking. Since each monitored event is usually characterized by a variety of features, high-dimensional datasets have been continuously generated. To extract value from these complex collections of data, different exploratory data mining algorithms can be used to discover hidden and non-trivial correlations among data. Frequent closed itemset mining is an effective but computational expensive technique that is usually used to support data exploration. Thanks to the spread of distributed and parallel frameworks, the development of scalable approaches able to deal with the so called Big Data has been extended to frequent itemset mining. Unfortunately, most of the current algorithms are designed to cope with low-dimensional datasets, delivering poor performances in those use cases characterized by high-dimensional data. This work introduces PaMPa-HD, a MapReduce-based frequent closed itemset mining algorithm for high dimensional datasets. An efficient solution has been proposed to parallelize and speed up the mining process. Furthermore, different strategies have been proposed to easily configure the algorithm parameter. The experimental results, performed on real-life high-dimensional use cases, show the efficiency of the proposed approach in terms of execution time, load balancing and robustness to memory issues.