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Comparative Analysis of Different Versions of Association Rule Mining Algorithm on AWS-EC2

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Advances in Visual Informatics (IVIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9429))

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Abstract

Data mining is an essential step of knowledge discovery in databases (KDD) process by analyzing the huge amount of data from different perspectives and summarizing it into potentially valuable, valid, novel, interesting, and previously unknown information. Due to the importance of extracting knowledge from the massive data repositories, data mining is an essential components in various fields. Association rule mining (ARM), is one of the most important and well researched techniques of data mining, It aims to extract essential relationships, frequent patterns, associations among itemsets in the transaction databases or other data repositories. Many algorithm have been proposed to find the frequent itemset efficiently. In this research, we have chosen four well established frequent itemset mining methods which are Apriori, Apriori TID, Eclat, and FP-Growth to analyze their performance on cloud environment. Cloud computing is a new paradigm to analyze big data efficiently and cost effectively. In this study we analyzed the algorithms on Amazon web service (AWS) platform using elastic cloud computing (EC2) service. We thereafter compare the four algorithms based on their execution time by varying the minimum support (min_sup) values.

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Acknowledgment

We wish to thank Universiti Kebangsaan Malaysia (UKM) and Ministry of Higher Education Malaysia for supporting this work by research Grants (ERGS/1/2013/ICT07/UKM/02/3).

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Authors and Affiliations

  1. Faculty of Information Science and Technology, Centre for Software Technology and Management, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor-DE, Malaysia

    Ahamed Lebbe Sayeth Saabith & Elankovan Sundararajan

  2. Faculty of Information Science and Technology, Center for Artificial Intelligence and Technology, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor-DE, Malaysia

    Azuraliza Abu Bakar

Authors
  1. Ahamed Lebbe Sayeth Saabith
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  2. Elankovan Sundararajan
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  3. Azuraliza Abu Bakar
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Corresponding author

Correspondence to Ahamed Lebbe Sayeth Saabith .

Editor information

Editors and Affiliations

  1. Fac Info Science and Techn, Universiti Kebangsaan Malaysia, Selangor, Malaysia

    Halimah Badioze Zaman

  2. University of Cambridge, Cambridge, United Kingdom

    Peter Robinson

  3. Center for Digital Video Process, Dublin 9, Ireland

    Alan F. Smeaton

  4. Computer Science and Information Enginee, National Central University, Jhongli City, Taiwan

    Timothy K. Shih

  5. Kingston University, Kingston upon Thames, United Kingdom

    Sergio Velastin

  6. Universiti Kebangsaan Malaysia, Institute of Visual Informatics, Bangi, Malaysia

    Azizah Jaafar

  7. Universiti Kebangsaan Malaysia, Institute of Visual Informatics, Bangi, Malaysia

    Nazlena Mohamad Ali

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Saabith, A.L.S., Sundararajan, E., Bakar, A.A. (2015). Comparative Analysis of Different Versions of Association Rule Mining Algorithm on AWS-EC2. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2015. Lecture Notes in Computer Science(), vol 9429. Springer, Cham. https://doi.org/10.1007/978-3-319-25939-0_6

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  • DOI: https://doi.org/10.1007/978-3-319-25939-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25938-3

  • Online ISBN: 978-3-319-25939-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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