Frequent patterns provide solutions to datasets that do not have well-structured feature vectors. However, frequent pattern mining is non-trivial since the number of unique patterns is exponential but many are non-discriminative and correlated. Currently, frequent pattern mining is performed in two sequential steps: enumerating a set of frequent patterns, followed by feature selection. Although many methods have been proposed in the past few years on how to perform each separate step efficiently, there is still limited success in eventually finding highly compact and discriminative patterns. The culprit is due to the inherent nature of this widely adopted two-step approach. This paper discusses these problems and proposes a new and different method. It builds a decision tree that partitions the data onto different

Abstract. Since counterexamples generated by model checking tools are only symptoms of faults in the model, a significant amount of manual work is required in order to locate the fault that is the root cause for the presence of counterexamples in the model. In this paper, we propose an automated method for explaining counterexamples that are symptoms of the occurrence of deadlocks in concurrent systems. Our method is based on an analysis of a set of counterexamples that can be generated by a model checking tool such as SPIN. By comparing the set of counterexamples with the set of correct traces that never deadlock, a number of sequences of actions are extracted that aid the model designer in locating the cause of the occurrence of a deadlock. We first argue that the obvious approach to extract such sequences which is by sequential pattern mining and by contrasting patterns that are typical for the deadlocking counterexample traces but not typical for non-deadlocking traces, fails due to the inherent complexity of the problem. We then propose to extract substrings of specific length that only occur in the set of counterexamples for explaining the occurrence of deadlocks. We use a number of case studies to show the effectiveness of our approach and to compare it with an alternative approach to the counterexample explanation problem.