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\(MC^2\): An Integrated Toolbox for Change, Causality and Motif Discovery

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Trends in Applied Knowledge-Based Systems and Data Science (IEA/AIE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9799))

Abstract

Time series are being generated continuously from all kinds of human endeavors. The ubiquity of time-series data generates a need for data mining and pattern discovery algorithms targeting this data format which is becoming of ever increasing importance. Three basic problems in mining time-series data are change point discovery, causality discovery and motif discovery. This paper presents an integrated toolbox that can be used to perform any of these tasks on multidimensional real-valued time-series using state of the art algorithms. The proposed toolbox provides practitioners in time-series analysis and data mining with several tools useful for data generation, preprocessing, modeling evaluation and mining of long sequences. The paper also reports real world applications that uses the toolbox in HRI, physiological signal processing, and human behavior modeling and understanding.

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Acknowledgments

The work reported in this paper was financially supported by JSPS KAKENHI Grant Number 15K12098, and AFOSR/AOARD Grant No. FA2386-14-1-0005.

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Authors and Affiliations

  1. Assiut University, Assiut, 71516, Egypt

    Yasser Mohammad & Toyoaki Nishida

  2. Kyoto University, Kyoto, Japan

    Yasser Mohammad & Toyoaki Nishida

Authors
  1. Yasser Mohammad
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  2. Toyoaki Nishida
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Corresponding author

Correspondence to Yasser Mohammad .

Editor information

Editors and Affiliations

  1. Iwate Prefectural University , Iwate, Japan

    Hamido Fujita

  2. Department Computer Science, Texas State University, San Marcos, Texas, USA

    Moonis Ali

  3. Universiti Teknologi Malaysis (UTM), Bahru, Malaysia

    Ali Selamat

  4. Iwate Prefectural University , Iwate, Japan

    Jun Sasaki

  5. Iwate Prefectural University , Iwate, Japan

    Masaki Kurematsu

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Mohammad, Y., Nishida, T. (2016). \(MC^2\): An Integrated Toolbox for Change, Causality and Motif Discovery. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_12

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  • DOI: https://doi.org/10.1007/978-3-319-42007-3_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42006-6

  • Online ISBN: 978-3-319-42007-3

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