Formal Concept Analysis for Trace Clustering in Process Mining

Abstract

Modeling user interaction in information systems (IS) using Process Mining techniques is an intriguing requirement for designers looking to optimize the use of various IS functionalities and make stored resources more accessible. Discovered models can thus be used in future work to present a set of recommendations to IS users. However, the large number of generated logs or user's traces result in complex models. To address this problem, in this paper, we propose a new methodology for grouping user traces prior to modeling using Formal Concept Analysis. The clustering method relies on the GALACTIC framework to generate relevant concepts, which are then used to select a specific concept for each trace using a distance measure. Considering a trace as a sequence, the proposed method generate concepts based on maximal common subsequences. The experimental part shows that our method successfully found the original clusters on a simulated dataset.