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HFIM: a Spark-based hybrid frequent itemset mining algorithm for big data processing

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Abstract

Frequent itemset mining is one of the data mining techniques applied to discover frequent patterns, used in prediction, association rule mining, classification, etc. Apriori algorithm is an iterative algorithm, which is used to find frequent itemsets from transactional dataset. It scans complete dataset in each iteration to generate the large frequent itemsets of different cardinality, which seems better for small data but not feasible for big data. The MapReduce framework provides the distributed environment to run the Apriori on big transactional data. However, MapReduce is not suitable for iterative process and declines the performance. We introduce a novel algorithm named Hybrid Frequent Itemset Mining (HFIM), which utilizes the vertical layout of dataset to solve the problem of scanning the dataset in each iteration. Vertical dataset carries information to find support of each itemsets. Moreover, we also include some enhancements to reduce number of candidate itemsets. The proposed algorithm is implemented over Spark framework, which incorporates the concept of resilient distributed datasets and performs in-memory processing to optimize the execution time of operation. We compare the performance of HFIM with another Spark-based implementation of Apriori algorithm for various datasets. Experimental results show that the HFIM performs better in terms of execution time and space consumption.

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Acknowledgements

The authors wish to express their appreciation to the editor and anonymous referees for many helpful suggestions that significantly improve this paper. This research work is supported by Department of Computer Science & Engineering, Indian Institute of Technology (ISM), Dhanbad, India. The authors would also like to express their gratitude and heartiest thanks to the Department of Computer Science and Engineering, Indian Institute of Technology (ISM), Dhanbad, India, for providing their research support.

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  1. Department of Computer Science and Engineering, Indian Institute of Technology (ISM), Dhanbad, Jharkhand, 826004, India

    Krishan Kumar Sethi & Dharavath Ramesh

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  1. Krishan Kumar Sethi
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  2. Dharavath Ramesh
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Correspondence to Dharavath Ramesh.

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Sethi, K.K., Ramesh, D. HFIM: a Spark-based hybrid frequent itemset mining algorithm for big data processing. J Supercomput 73, 3652–3668 (2017). https://doi.org/10.1007/s11227-017-1963-4

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