Abstract
Periodic Frequent patterns (PFPs) are an important class of regularities that exist in a transactional database. In the literature, pattern growth-based approaches to mine PFPs have be proposed by considering a single machine. In this paper, we propose a Map-Reduce framework to mine PFPs by considering multiple machines. We have proposed a parallel algorithm by including the step of distributing transactional identifiers among the machines. Further, the notion of partition summary has been proposed to reduce the amount of data shuffled among the machines. Experiments on Apache Spark’s distributed environment show that the proposed approach speeds up with the increase in number of machines and the notion of partition summary significantly reduces the amount of data shuffled among the machines.
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Acknowledgment
This research was partly supported by the program Research and Development on Real World Big Data Integration and Analysis of the Ministry of Education, Culture, Sports, Science and Technology, and RIKEN, Japan. We acknowledge K. Amulya for her contribution in implementation of the idea.
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Anirudh, A., Kiran, R.U., Reddy, P.K., Toyoda, M., Kitsuregawa, M. (2017). An Efficient Map-Reduce Framework to Mine Periodic Frequent Patterns. In: Bellatreche, L., Chakravarthy, S. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2017. Lecture Notes in Computer Science(), vol 10440. Springer, Cham. https://doi.org/10.1007/978-3-319-64283-3_9
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DOI: https://doi.org/10.1007/978-3-319-64283-3_9
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