Issue No. 08 - August (2006 vol. 18)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2006.123
G. Greco , Dept. of Math., Calabria Univ.
Process mining techniques have recently received notable attention in the literature; for their ability to assist in the (re)design of complex processes by automatically discovering models that explain the events registered in some log traces provided as input. Following this line of research, the paper investigates an extension of such basic approaches, where the identification of different variants for the process is explicitly accounted for, based on the clustering of log traces. Indeed, modeling each group of similar executions with a different schema allows us to single out "conformant" models, which, specifically, minimize the number of modeled enactments that are extraneous to the process semantics. Therefore, a novel process mining framework is introduced and some relevant computational issues are deeply studied. As finding an exact solution to such an enhanced process mining problem is proven to require high computational costs, in most practical cases, a greedy approach is devised. This is founded on an iterative, hierarchical, refinement of the process model, where, at each step, traces sharing similar behavior patterns are clustered together and equipped with a specialized schema. The algorithm guarantees that each refinement leads to an increasingly sound mDdel, thus attaining a monotonic search. Experimental results evidence the validity of the approach with respect to both effectiveness and scalability
Companies, Data mining, Management information systems, Customer relationship management, Supply chain management, Enterprise resource planning, Computer Society, Computational efficiency, Clustering algorithms, Iterative algorithms,association rules., Process mining, data mining, workflow management, clustering, classification
G. Greco, A. Guzzo, L. Pontieri, D. Sacca, "Discovering expressive process models by clustering log traces", IEEE Transactions on Knowledge & Data Engineering, vol. 18, no. , pp. 1010-1027, August 2006, doi:10.1109/TKDE.2006.123