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Rough Set-Based Text Mining from a Large Data Repository of Experts’ Diagnoses for Power Systems

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Book cover Intelligent Decision Technologies 2017 (IDT 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 73))

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Abstract

Usually it is hard to classify the situation where uncertainty of randomness and fuzziness exists simultaneously. This paper presents a rough set approach applying fuzzy random variable and statistical t-test to text-mine a large data repository of experts’ diagnoses provided by a Japanese power company. The algorithms of rough set and statistical t-test are used to distinguish whether a subset can be classified in the object set or not. The expected-value-approach is also applied to calculate the fuzzy value with probability into a scalar value.

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Acknowledgement

This work was supported partially by Petroleum Research Fund (PRF) No. 0153AB-A33 through Universiti Teknologi PETRONAS.

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Authors and Affiliations

  1. Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia

    Junzo Watada

  2. Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia

    Shing Chiang Tan

  3. Faculty of Economics, Shimonoseki City University, Gakuencho, Shimonoseki, Japan

    Yoshiyuki Matsumoto

  4. Department of Fundamental Mathematics and Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia

    Pandian Vasant

Authors
  1. Junzo Watada
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  2. Shing Chiang Tan
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  3. Yoshiyuki Matsumoto
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  4. Pandian Vasant
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Corresponding author

Correspondence to Junzo Watada .

Editor information

Editors and Affiliations

  1. Maritime University , Gdynia, Poland

    Ireneusz Czarnowski

  2. Bournemouth University and KES International, Poole, Dorset, United Kingdom

    Robert J. Howlett

  3. University of Canberra, Canberra, Aust Capital Terr, Australia

    Lakhmi C. Jain

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Watada, J., Tan, S.C., Matsumoto, Y., Vasant, P. (2018). Rough Set-Based Text Mining from a Large Data Repository of Experts’ Diagnoses for Power Systems. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2017. IDT 2017. Smart Innovation, Systems and Technologies, vol 73. Springer, Cham. https://doi.org/10.1007/978-3-319-59424-8_13

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  • DOI: https://doi.org/10.1007/978-3-319-59424-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59423-1

  • Online ISBN: 978-3-319-59424-8

  • eBook Packages: EngineeringEngineering (R0)

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