We deal with a sequencing problem that arises when there are multiple repair actions available to fix a broken man-made system and the true cause of the system failure is uncertain. The system is formally described by a probabilistic model, and it is to be repaired by a sequence of troubleshooting actions designed to identify the cause of the malfunction and fix the system. The task is to find a course of repair with minimal expected cost. We propose a binary integer programming formulation for the problem. This can be used to solve the problem directly or to compute lower bounds of the minimal expected cost using linear programming relaxation. We also present three greedy algorithms for computing initial feasible solutions.
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.