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.