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A Framework to Cluster Temporal Data Using Personalised Modelling Approach

  • Muhaini Othman
  • Siti Aisyah Mohamed
  • Mohd Hafizul Afifi Abdullah
  • Munirah Mohd Yusof
  • Rozlini Mohamed
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 700)

Abstract

This research paper is focused on the framework design of temporal data by using personalised modelling approach in order to cluster the temporal data. Real world problem on flood occurrences is used as a case study focusing only in Malaysia region. The data are designed according to the criteria needed for temporal data clustering, tested with three clustering techniques including K-means, X-means, and K-medoids. Rapid Miner is used for conducting the clustering processes. Finally, the result from each clustering method is compared to conclude and justify the best clustering approach for clustering temporal data.

Keywords

Personalised modelling Temporal data Clustering Flood case study 

Notes

Acknowledgements

This work is supported by the Fundamental Research Grant (Vot 1612) from Ministry of Higher Education Malaysia, Universiti Tun Hussein Onn Malaysia (UTHM), and GATES IT Solution Sdn. Bhd.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Muhaini Othman
    • 1
  • Siti Aisyah Mohamed
    • 1
  • Mohd Hafizul Afifi Abdullah
    • 1
  • Munirah Mohd Yusof
    • 1
  • Rozlini Mohamed
    • 1
  1. 1.Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn MalaysiaBatu PahatMalaysia

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