[ABA15] Improving process models discovery using AXOR Clustering Algorithm

Conférence Internationale avec comité de lecture : International Conference on Information Science and Applications, February 2015, Vol. 339(Springer Berlin Heidelberg), pp.pp 623-629, Series Lecture Notes in Electrical Engineering, Berlin, (DOI: 10.1007/978-3-662-46578-3_73)

Mots clés: Process mining, process discovery, clustering, fitness

Résumé: The goal of process mining is to discover process models from event logs. Real-life processes tend to be less structured and more exible. Classical process mining algorithms face to unstructured processes, generate spaghetti-like process models which are hard to comprehend. One way to cope with these models consists to divide the log into clus- ters in order to analyze reduced sets of cases. In this paper, we propose a new clustering approach where cases are restricted to activity profiles. We evaluate the quality of the formed clusters using established fitness and comprehensibility metrics on the basis distance using logical XOR operator. throwing a significant real-life case study, we illustrate our ap- proach, and we show its interest especially for flexible environments


Equipe: sys , isid
Collaboration: CEDRIC


@inproceedings {
title="{Improving process models discovery using AXOR Clustering Algorithm}",
author=" H. Ariouat and K. Barkaoui and J. Akoka ",
booktitle="{International Conference on Information Science and Applications}",
edition="Kuinam J. Kim",
series="Lecture Notes in Electrical Engineering",
issue=Springer Berlin Heidelberg,
pages="pp 623-629",
address=" Berlin",