Improving process models discovery using AXOR clustering algorithm

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 339)

Abstract

The goal of process mining is to discover process models from event logs. Real-life processes tend to be less structured and more flexible. 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 clusters 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 approach, and we show its interest especially for flexible environments.

Keywords

Process mining process discovery clustering fitness 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    A. K.Jain, and R.C.Dubes: Algorithms for Clustering Data. Prentice-Hall Inc. (1988).Google Scholar
  2. 2.
    W.M.P. van der Aalst, H.A. Reijers, A.J.M.M. Weijters, B.F.van Dongen, A.K.A. de Medeiros, M. Song, and H.M.W. Verbeek: Business Process Mining: An Industrial Application. Info. Sys. 32(5) 713-732 (2007).Google Scholar
  3. 3.
    R.S. Mans, M.H. Schonenberg, M. Song, W.M.P. van der Aalst, and P.J.M. Bakker, Process Mining in Health Care. In L. Azevedo and A.R. Londral, editors, International Conference on Health Informatics (HEALTHINF’08), pages 118-125. IEEE Computer Society, (2008).Google Scholar
  4. 4.
    W.M.P. van der Aalst, A.J.M.M. Weijters, Process Mining: A Research Agenda, Computers in Industry, 53(3):231-244, (2004).Google Scholar
  5. 5.
    M. Song, and C.W. Gunther, and W.M.P. van der Aalst: Trace Clustering in Process Mining, BPM Workshops. (2008).Google Scholar
  6. 6.
    G. Greco, and A. Guzzo, and L. Pontieri, and D. Sacca: Discovering Expressive Process Models by Clustering Log Traces. IEEE Trans. Knowl. Data Eng. 1010-1027 (2006).Google Scholar
  7. 7.
    A.K.A. de Medeiros, and A. Guzzo, and G. Greco, and W.M.P. van der Aalst, and A.J.M.M. Weijters, and B.F. van Dongen, and D. Sacca: Process Mining Based on Clustering: A Quest for Precision. BPM Workshops 17-29 (2007).Google Scholar
  8. 8.
    R.P. Jagadeesh Chandra Bose and W.M.P. van der Aalst, Context Aware Trace Clustering: Towards Improving Process Mining Results, Proc. SIAM Int’l Conf. Data Mining (SDM), pp. 401-412, (2009).Google Scholar
  9. 9.
    R.P. Jagadeesh ChandraBose and W.M.P. van der Aalst, Trace Clustering Based on Conserved Patterns: Towards Achieving Better Process Models, Proc. Int’l Business Process Management Workshops, pp. 170-181, (2009).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Lab. CedricCnamParisFrance

Personalised recommendations