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A Note on Covariances for Categorical Data

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Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents (IDEAL 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1983))

Abstract

Generalization of the covariance concept is discussed for mixed categorical and numerical data. Gini’s definition of variance for categorical data gives us a starting point to address this issue. The value difference in the original definition is changed to a vector in value space, giving a new definition of covariance for categorical and numerical data. It leads to reasonable correlation coefficients when applied to typical contingency tables.

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References

  1. Takeuchi, K. et al. (eds.): Encyclopedia of Statistics (in Japanese). Toyo Keizai Shinpou Tokyo, (1990)

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  3. Gini, C.W.: Variability and Mutability, contribution to the study of statistical distributions and relations. Studi Economico-Giuridici della R. Universita de Cagliari (1912). Reviewed in: Light, R.J., Margolin, B.H.: An Analysis of Variance for Categorical Data. J. American Statistical Association, 66 (1971) 534–544

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  4. Okada, T.: Sum of Squares Decomposition for Categorical Data. Kwansei Gakuin Studies in Computer Science, 14 (1999) 1–6

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Author information

Authors and Affiliations

  1. Center for Information & Media Studies, Kwansei Gakuin University, 1-1-155 Uegahara, 662-8501, Nishinomiya, Japan

    Takashi Okada

Authors
  1. Takashi Okada
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

    Kwong Sak Leung  & Lai-Wan Chan  & 

  2. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong

    Helen Meng

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© 2000 Springer-Verlag Berlin Heidelberg

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Okada, T. (2000). A Note on Covariances for Categorical Data. In: Leung, K.S., Chan, LW., Meng, H. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. IDEAL 2000. Lecture Notes in Computer Science, vol 1983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44491-2_23

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  • DOI: https://doi.org/10.1007/3-540-44491-2_23

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41450-6

  • Online ISBN: 978-3-540-44491-6

  • eBook Packages: Springer Book Archive

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