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An Extension of Totohasina’s Normalization Theory of Quality Measures of Association Rules

Author

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  • Armand
  • André Totohasina
  • Daniel Rajaonasy Feno

Abstract

In the context of binary data mining, for unifying view on probabilistic quality measures of association rules, Totohasina’s theory of normalization of quality measures of association rules primarily based on affine homeomorphism presents some drawbacks. Indeed, it cannot normalize some interestingness measures which are explained below. This paper presents an extension of it, as a new normalization method based on proper homographic homeomorphism that appears most consequent.

Suggested Citation

  • Armand & André Totohasina & Daniel Rajaonasy Feno, 2019. "An Extension of Totohasina’s Normalization Theory of Quality Measures of Association Rules," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2019, pages 1-7, January.
    • Handle: RePEc:hin:jijmms:7829805
    DOI: 10.1155/2019/7829805
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    Cited by:

    1. Souhila Ghanem & Raphaël Couturier & Pablo Gregori, 2021. "An Accurate and Easy to Interpret Binary Classifier Based on Association Rules Using Implication Intensity and Majority Vote," Mathematics, MDPI, vol. 9(12), pages 1-12, June.

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