Pattern-growth based frequent serial episode discovery

Article history: Received 28 October 2011 Received in revised form 23 June 2013 Accepted 25 June 2013 Available online 13 July 2013 Frequent episode discovery is a popular framework for pattern discovery from sequential data. It has found many applications in domains like alarmmanagement in telecommunication networks, fault analysis in the manufacturing plants, predicting user behavior in web click streams and so on. In this paper, we address the discovery of serial episodes. In the episodes context, there have been multiple ways to quantify the frequency of an episode. Most of the current algorithms for episode discovery under various frequencies are apriori-based level-wise methods. These methods essentially perform a breadth-first search of the pattern space. However currently there are no depth-first based methods of pattern discovery in the frequent episode framework under many of the frequency definitions. In this paper, we try to bridge this gap. We provide new depth-first based algorithms for serial episode discovery under non-overlapped and total frequencies. Under non-overlapped frequency, we present algorithms that can take care of span constraint and gap constraint on episode occurrences. Under total frequency we present an algorithm that can handle span constraint.Weprovide proofs of correctness for the proposedalgorithms.Wedemonstrate the effectiveness of the proposed algorithms by extensive simulations. We also give detailed run-time comparisons with the existing apriori-based methods and illustrate scenarios under which the proposed pattern-growth algorithms perform better than their apriori counterparts. © 2013 Elsevier B.V. All rights reserved.