Browse > Article

Improving Process Mining with Trace Clustering  

Song, Min-Seok (Faculty of Technology Management, Eindhoven University of Technology)
Gunther, C.W. (Faculty of Technology Management, Eindhoven University of Technology)
van der Aalst, W.M.P. (Faculty of Technology Management, Eindhoven University of Technology)
Jung, Jae-Yoon (Department of Industrial Engineering, Kyung Hee University)
Publication Information
Journal of Korean Institute of Industrial Engineers / v.34, no.4, 2008 , pp. 460-469 More about this Journal
Abstract
Process mining aims at mining valuable information from process execution results (called "event logs"). Even though process mining techniques have proven to be a valuable tool, the mining results from real process logs are usually too complex to interpret. The main cause that leads to complex models is the diversity of process logs. To address this issue, this paper proposes a trace clustering approach that splits a process log into homogeneous subsets and applies existing process mining techniques to each subset. Based on log profiles from a process log, the approach uses existing clustering techniques to derive clusters. Our approach are implemented in ProM framework. To illustrate this, a real-life case study is also presented.
Keywords
Process Mining; Trace Clustering; Workflow; Data Mining; SOM;
Citations & Related Records
연도 인용수 순위
  • Reference
1 van der Aalst, W. M. P., H. A. Reijers, A. J. M. M. Weijters, B. F. van Dongen, A. K. Alves de Medeiros, M. Song, and H. M. W. Verbeek (2007), Business Process Mining : An Industrial Application, Information Systems, Information Systems, 32(5), 713-732   DOI   ScienceOn
2 van der Aalst, W. M. P., et al. (2007), ProM 4.0 : Comprehensive Support for Real Process Analysis, Proc. 28th Int'l Conf. on Applications and Theory of Petri Nets and Other Models of Concurrency (ICATPN 2007), Lecture Notes on Computer Science, 4546, 484- 494
3 Jansen-Vullers, M. H., van der Aalst, W. M. P., and Rosemann, M. (2006), Mining Configurable Enterprise Information Systems, Data and Knowledge Engineering, 56(3), 195-244   DOI   ScienceOn
4 Kohonen, T. (1982), Self-organation formation of topologically correct feature maps, Biological Cybernetics, 43(1), 59-69   DOI
5 de Medeiros, A. K. Alves, Weijters, A. J. M. M., and van der Aalst, W. M. P. (2007), Genetic Process Mining : An Experimental Evaluation, Data Mining and Knowledge Discovery, 14(2), 245-304   DOI   ScienceOn
6 Dumas, M., van der Aalst, W. M. P., and ter Hofstede, A. H. M. (2005), Process-Aware Information Systems: Bridging People and Software through Process Technology, Wiley and Sons
7 Gunther, C. W. and van der Aalst, W. M. P. (2007), Fuzzy Mining -Adaptive Process Simplication Based on Multi-Perspective Metrics, In G. Alonso, P. Dadam, and M. Rosemann, editors, International Conference on Business Process Management(BPM 2007), Lecture Notes on Computer Science, 4714, 328-343
8 Heyer, L. J., Kruglyak, S., and Yooseph, S. (1999), Exploring Expression Data: Identification and Analysis of Coexpressed Genes, Genome Research, 9(11), 1106-1115   DOI
9 van der Aalst, W. M. P. and Basten, T. (2002), Inheritance of workflows : an approach to tackling problems related to change, Theoretical Computer Science, 270(1), 125-203   DOI   ScienceOn
10 van der Aalst, W. M. P., Weijters, A. J. M. M., and Maruster, L. (2004), Workow Mining : Discovering Process Models from Event Logs, IEEE Transactions on Knowledge and Data Engineering, 16 (9), 1128-1142   DOI   ScienceOn
11 Greco, G., Guzzo, A., and Pontieri, L. (2006), Discovering Expressive Process Models by Clustering Log Traces, IEEE Transactions on Knowledge and Data Engineering, 18(8), 1010-1027   DOI   ScienceOn
12 Jung, J.-Y., PROCL : A Process Log Clustering System, The Journal of Society for e-Business Studies, 13(2), 181-194
13 Rozinat, A. and van der Aalst, W. M. P. (2008), Conformance checking of processes based on monitoring real behavior, Information Systems, 33(1), 64-95   DOI   ScienceOn
14 Rozinat, A. and W. M. P. van der Aalst (2006), Decision Mining in ProM, Proc. 4th Int. Conf. on Business Process Management, 420-425
15 Kaufman, L. and Rousseeuw, P. J. (1990), Finding Groups in Data : An Introduction to Cluster Analysis
16 van der Aalst, W. M. P., Reijers, H. A., and Song, M. (2005), Discovering Social Networks from Event Logs, Computer Supported Cooperative work, 14(6), 549-593   DOI
17 Lloyd, S. P. (1982), Least squares quantization in PCM, IEEE Transactions on Information Theory, 2, 129-137