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Association Rule Mining Using Time Series Data for Malaysia Climate Variability Prediction

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Advances in Visual Informatics (IVIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10645))

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Abstract

Many studies have been conducted to determine how data mining can be used in predicting climate change. Previous studies showed many data mining methods have been used in related to climate prediction, however classification and clustering methods are widely used to generate the climate prediction model. In this study, Association Rule Mining (ARM) is used to discover hidden rules in time series climate data from previous years and to analyze the relationship between the discovered rules. The dataset used in this study is a set of weather data from the Petaling Jaya observation station in Selangor for the year 2013 to 2015. This paper aims to utilize ARM for extracting behavioural patterns within the climate data that can be used to develop the prediction model for climate variability. The proposed framework is developed to provide a better approach in understanding how ARM can be used to find meaningful patterns in the climate data and generate rules that can be used to build a prediction model.

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Acknowledgements

Authors would like to thank Institute of Climate Change, The National University of Malaysia for providing the climate data to be used in this study.

Author information

Authors and Affiliations

  1. Institute of Visual Informatics, National University of Malaysia, 43600, Bangi, Selangor, Malaysia

    Rabiatul A. A. Rashid & Puteri N. E. Nohuddin

  2. Department of Computer Science, Faculty of Science and Defence Technology, National Defence University of Malaysia, Sungai Besi Camp, 57000, Kuala Lumpur, Malaysia

    Zuraini Zainol

Authors
  1. Rabiatul A. A. Rashid
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  2. Puteri N. E. Nohuddin
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  3. Zuraini Zainol
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Corresponding author

Correspondence to Rabiatul A. A. Rashid .

Editor information

Editors and Affiliations

  1. Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    Halimah Badioze Zaman

  2. University of Cambridge, Cambridge, United Kingdom

    Peter Robinson

  3. Dublin City University, Dublin, Ireland

    Alan F. Smeaton

  4. National Central University, Jhongli, Taiwan

    Timothy K. Shih

  5. Carlos III University of Madrid, Madrid, Spain

    Sergio Velastin

  6. Toyo University, Kawagoe, Japan

    Tada Terutoshi

  7. Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    Azizah Jaafar

  8. Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    Nazlena Mohamad Ali

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A. Rashid, R.A., Nohuddin, P.N.E., Zainol, Z. (2017). Association Rule Mining Using Time Series Data for Malaysia Climate Variability Prediction. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2017. Lecture Notes in Computer Science(), vol 10645. Springer, Cham. https://doi.org/10.1007/978-3-319-70010-6_12

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  • DOI: https://doi.org/10.1007/978-3-319-70010-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70009-0

  • Online ISBN: 978-3-319-70010-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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