The Cohen’s kappa coefficient is a widely accepted mecisure of agreement on categorical variables and has replaced some older simpler measures. Observational and statistical properties of the kappa coefficient in 2 x 2 tables are investigated. The asymmetrical measure “Cohenized implication” is proposed. The decomposition of the symmetrical measure kappa into two asymmetrical components is shown. These statistically motivated measures are discussed as weakened forms of strict logical notions of equivalence and implication. Applications of kappa and “Cohenized implication” are recommended; on the one hand in the medical research as a supplement to traditional measures of sensitivity and speciíity, on the other hand as quantifiers in the GUHA proceduře ASSOC as a statistically contemporary operationalization of the weakened equivalence.
KL-Miner [9] is a datarnining procedure that, given input data matrix
M. and a set of parameters, generates patterns of the form R ~ C/7. Here R and C are categorial attributes corresponding to the columns of M, and 7 is a Boolean condition defined in terms of the remaining colums of Ai. The pattern R C means that R and C are strongly correlated on the submatrix of M formed by all the rows of M that satisfy 7. What is meant by “strong correlation” and how are R, C and 7 generated is determined by the input parameters of the procedure. KL-Miner conforms to the GUHA principle forinulated in [1]. It revives two older GUHA procedures described in [2]; it is very much related to CORREL and contains a new implementation of COLLAPS as a module.
In this paper, we mention the motivation that leads to designing of KL-Miner, describing our new implementation of COLLAPS and giving application exarnples that illustrate the main features of KL-Miner.