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

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.

Toward a Theory of Normalizing Function of Interestingness Measure of Binary Association Rules

“…The following section will attempt to answer such question. Remember that this paper is the logical continuation of the paper [18]. .…”

“…which is such that = +∞, = 1, = 0. Using research theories of normalization coefficients , , , and in [18] we have = −1, = 1, = 1, and = −1. As a result, by replacing , , , , and by their values in the expression 3…”

An Accurate and Easy to Interpret Binary Classifier Based on Association Rules Using Implication Intensity and Majority Vote

“…Depending on the random sampling scheme, the distribution of N ab can be Binomial or Poisson, and a Gaussian approximation is feasible for large sample sizes [11]. Other probabilistic approaches to define the interestingness of rules can be seen in [33]. Example 1.…”