TWINCLE : A Constrained Sequential Rule Mining Algorithm for Event Logs

Discovering workflow patterns in event-logs is important for many organizations to understand and optimize organizational processes. Although numerous algorithms have been proposed in the literature to discover patterns in sequences of symbols, most of them are inadequate to discover patterns in rich event-log data. In this paper, motivated by the analysis of patient pathways in the health domain, a rich type of event logs, called activity-cost event logs, is considered where each event is associated with a cost. The paper formalizes the problem of mining interesting low-cost patterns in these logs by combining novel concepts of penalties (activity costs) and consistency of patterns, with traditional measures of confidence, length, and time. Furthermore, to extract these patterns efficiently from event logs, an algorithm named TWINCLE (Time-WINdow, Cost and LEngth constrained sequential rule mining) is proposed. Experiments carried out on benchmark datasets and real-life healthcare event logs show that proposed algorithm is efficient and can discover interesting patterns.