Mining a Minimal Set of Behavioral Patterns using Incremental Evaluation
Mehdi Acheli Daniela Grigori Matthias Weidlich
Process mining provides methods to analyse event logs generated by
information systems during the execution of processes. It thereby supports the
design, validation, and execution of processes in domains ranging from
healthcare, through manufacturing, to e-commerce. To explore the regularities
of flexible processes that show a large behavioral variability, it was
suggested to mine recurrent behavioral patterns that jointly describe the
underlying process. Existing approaches to behavioral pattern mining, however,
suffer from two limitations. First, they show limited scalability as
incremental computation is incorporated only in the generation of pattern
candidates, but not in the evaluation of their quality. Second, process
analysis based on mined patterns shows limited effectiveness due to an
overwhelmingly large number of patterns obtained in practical application
scenarios, many of which are redundant. In this paper, we address these
limitations to facilitate the analysis of complex, flexible processes based on
behavioral patterns. Specifically, we improve COBPAM, our initial behavioral
pattern mining algorithm, by an incremental procedure to evaluate the quality
of pattern candidates, optimizing thereby its efficiency. Targeting a more
effective use of the resulting patterns, we further propose pruning strategies
for redundant patterns and show how relations between the remaining patterns
are extracted and visualized to provide process insights. Our experiments with
diverse real-world datasets indicate a considerable reduction of the runtime
needed for pattern mining, while a qualitative assessment highlights how
relations between patterns guide the analysis of the underlying process.