Understanding spaghetti models with sequence clustering for ProM

Lecture Notes in Business Information Processing

Volumn 43 LNBIP, Issue , 2010, Pages 92-103

  Veiga, Gabriel M ^a   Ferreira, Diogo R ^a  

^a INSTITUTO SUPERIOR TÉCNICO   (Portugal)

Author keywords

Event Logs; Hierarchical Clustering; Markov Chains; Preprocessing; Process Mining; Process Models; ProM; Sequence Clustering

Indexed keywords

DATA MINING; ENTERPRISE RESOURCE MANAGEMENT; MARKOV PROCESSES; SECURITY OF DATA;

BEHAVIORAL PATTERNS; COMPLEX MODEL; HIER-ARCHICAL CLUSTERING; MARKOV CHAIN; MARKOV CHAINS; PROCESS MINING; PROCESS MODEL; REAL-WORLD; REDUCED SETS; TWO STAGE;

CLUSTER ANALYSIS;

EID: 77953963552     PISSN: 18651348     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-12186-9_10     Document Type: Conference Paper

References (14)

1. van Dongen, B., de Medeiros, A.A., Verbeek, H., Weijters, A., van der Aalst, W.: The proM framework: A new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444-454. Springer, Heidelberg (2005)
2. Greco, G., Guzzo, A., Pontieri, L., Saccá, D.: Mining expressive process models by clustering workflow traces. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 52-62. Springer, Heidelberg (2004)
3. de Medeiros, A.K.A., Guzzo, A., Greco, G., van der Aalst, W.M.P., Weijters, A.J.M.M.T., van Dongen, B.F., Saccà, D.: Process mining based on clustering: A quest for precision. In: ter Hofstede, A.H.M., Benatallah, B., Paik, H.-Y. (eds.) BPM Workshops 2007. LNCS, vol. 4928, pp. 17-29. Springer, Heidelberg (2008)
4. Weijters, A., van der Aalst, W., de Medeiros, A.A.: Process mining with the heuristicsminer algorithm. BETA Working Paper Series WP 166, Eindhoven University of Technology (2006)
5. Song, M., Günther, C., van der Aalst, W.: Trace clustering in process mining. In: Proceedings of the 4th Workshop on Business Process Intelligence (BPI 2008), BPM Workshops 2008, Milan, September 1 (2008)
6. Bose, R.P.J.C., van der Aalst,W.M.P.: Context aware trace clustering: Towards improving process mining results. In: SDM, pp. 401-412. SIAM, Philadelphia (2009)
7. Enright, A.J., Ouzounis, C.: Generage: a robust algorithm for sequence clustering and domain detection. Bioinformatics 16(5), 451-457 (2000)
8. Cadez, I., Heckerman, D., Meek, C., Smyth, P., White, S.: Model-based clustering and visualization of navigation patterns on a web site. Data Mining and Knowledge Discovery 7(4), 399-424 (2003)
9. Ferreira, D.: Applied sequence clustering techniques for process mining. In: Cardoso, J., van der Aalst, W. (eds.) Handbook of Research on Business Process Modeling. IGI Global (2009)
10. Ferreira, D., Zacarias, M., Malheiros, M., Ferreira, P.: Approaching process mining with sequence clustering: Experiments and findings. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 360-374. Springer, Heidelberg (2007)
11. Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39(1), 1-38 (1977)
12. Enright, A.J., van Dongen, S., Ouzounis, C.: An efficient algorithm for large-scale detection of protein families. Nucleic Acids Research 30(7), 1575-1584 (2002)
13. Tang, Z., MacLennan, J.: Data Mining with SQL Server 2005, ch. 8 pp. 209-227. Wiley Publishing, Inc., Chichester (2005)
14. van Dongen, B., van der Aalst, W.: A meta model for process mining data. In: Casto, J., Teniente, E. (eds.) Proceedings of the CAiSE 2005 Workshops (EMOI-INTEROP Workshop), FEUP, Porto, Portugal, vol. 2, pp. 309-320 (2005)