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Perception Based Time Series Data Mining for Decision Making

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Book cover Theoretical Advances and Applications of Fuzzy Logic and Soft Computing

Part of the book series: Advances in Soft Computing ((AINSC,volume 42))

Abstract

In this paper, several aspects of perception based time series data mining based on the methodology of computing with words and perceptions are discusses. First, we consider possible approaches to precisiate perception based patterns in time series data bases and types of fuzzy constraints used in such precisiation. Next, several types of associations in time series data bases and the possible approaches to convert these associations in generalized constraint rules are discussed. Finally, we summarize the methods of translation of expert knowledge and retranslation of solutions.

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Authors and Affiliations

  1. Research Program in Applied Mathematics and Computing (PIMAyC), Mexican Petroleum Institute, Eje Central Norte Lazaro Cardenas 152, 07730, D.F., México

    Ildar Batyrshin & Leonid Sheremetov

Authors
  1. Ildar Batyrshin
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  2. Leonid Sheremetov
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Editor information

Oscar Castillo Patricia Melin Oscar Montiel Ross Roberto Sepúlveda Cruz Witold Pedrycz Janusz Kacprzyk

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Batyrshin, I., Sheremetov, L. (2007). Perception Based Time Series Data Mining for Decision Making. In: Castillo, O., Melin, P., Ross, O.M., Sepúlveda Cruz, R., Pedrycz, W., Kacprzyk, J. (eds) Theoretical Advances and Applications of Fuzzy Logic and Soft Computing. Advances in Soft Computing, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72434-6_22

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  • DOI: https://doi.org/10.1007/978-3-540-72434-6_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72433-9

  • Online ISBN: 978-3-540-72434-6

  • eBook Packages: EngineeringEngineering (R0)

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