Cluster sequence mining from event sequence data and its application to damage correlation analysis

Abstract

We propose a novel mining algorithm called cluster sequence mining (CSM) to extract cluster pairs with occurrence correlation from event sequence data. CSM extracts patterns with a pair of clusters that satisfies space proximity of the individual clusters and temporal proximity between events from different clusters in time intervals. CSM extends a unique co-occurring cluster mining (CCM) algorithm by considering the order of event occurrences and distribution of time intervals. The probability density of time intervals is inferred using Bayesian inference for robustness against uncertainty. To improve inference accuracy of the density function of time intervals, we utilize the idea of dynamic programming (DP) matching to obtain the correspondence between multiple event occurrences. With an experiment using synthetic data, we confirm that CSM is capable of extracting clusters with a high F-measure and low estimation error of the time interval distribution even under uncertainty. In addition, we find that DP matching can improve the inference accuracy of the density function of time intervals. Finally, CSM is applied to a real-world acoustic emission event sequence data set to evaluate damage interactions in a fuel cell.