Pattern Structures for Understanding Episode Patterns

We investigate an application of pattern structures for understanding episodes, which are labeled directed acyclic graphs representing event transitions. Since typical episode mining algorithms generate a huge number of similar episodes, we need to summarize them or to obtain compact representations of them for applying the outcome of mining to various problems. Though such problems have been well-studied for itemsets, summarization of episodes is still understudied. For a class called diamond episodes, we first provide a pattern structure based on hierarchy of events to obtain small groups of episodes in the form of pattern concepts and lattice structures. To find a summary via pattern concepts, we design an utility function for scoring concepts. After ranking concepts using some function and lattice structures, we try to sample a set of pattern concepts of high scores as a summary of episodes. We report our experimental results of our patten structure, and a ranking result of our simple utility function. Last we discuss pattern concept lattices and their applications for summarization problems.