Data Mining for Process Modeling: A Clustered Process Discovery Approach

Abstract

Process mining has emerged as a new scientific research topic on the interface between process modeling and event data gathering. In the search for process models that best fit to reality, the process discovery approach of creating referential processes from observed behavior. However, despite these methods showing relevant results, when faced with noisy and divergent tendencies they end up producing limited results. This work proposes the application of process discovery technique, combined to cluster technique k-means, to generate new process models, considering its conformance checking measures. The proposed solution is applied to an ad hoc workflow. And as a result, the use of the clustering techniques coupled with process discovery showed significant gains in the generation of process models, unlike the standard approach.

Using Machine Learning in Business Process Re-Engineering

“…Nonetheless, data science focuses on data-related subjects, such as data quality, without considering business process-related issues, such as improving process performance. Therefore, process mining emerged to reduce the gap between data science and process science [17].…”