Abstract
Process mining refers to the extraction process models from event logs. Traditional process mining algorithms have problems dealing with event logs that are produced from unstructured real-life processes and generate spaghetti-like and incomprehensible process models. One means making traces more structural is to extract commonly used process model constructs (common patterns) in the event log and transform traces basing on such constructs. Another way of pre-processing traces is to categorize traces in event log into clusters such that process traces in each cluster can be adequately represented by a process model. Nevertheless, current approaches for trace clustering have many problems such as ignoring context process and huge computational overhead. In this paper, suffix-tree is firstly utilized for discovering common patterns. The traces in event log are transformed with common patterns. Thereafter suffix-trees are applied to categorize transformed traces. The trace clustering algorithm has a linear-time computational complexity. The process models mined from the clustered traces show a high degree of fitness and comprehensibility.
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References
van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: Workflow Mining: Discovering Process Models from Event Logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)
Greco, G., Guzzo, A., Pontieri, L.: Mining Hierarchies of Models: From Abstract Views to Concrete Specifications. In: van der Aalst, W.M.P., Benatallah, B., Casati, F., Curbera, F. (eds.) BPM 2005. LNCS, vol. 3649, pp. 32–47. Springer, Heidelberg (2005)
Greco, G., Guzzo, A., Pontieri, L.: Mining Taxonomies of Process Models. Data Knowl. Eng. 67(1), 74 (2008)
Jagadeesh Chandra Bose, R.P., van der Aalst, W.M.P.: Abstractions in Process Mining: A Taxonomy of Patterns. In: Dayal, U., Eder, J., Koehler, J., Reijers, H.A. (eds.) BPM 2009. LNCS, vol. 5701, pp. 159–175. Springer, Heidelberg (2009)
Jain, A.K., Murty, M.N., Flynn: Data Clustering: A Review. ACM Computing Surveys 31(3), 264–323 (1999)
Song, M., Günther, C.W., van der Aalst, W.M.P.: Trace Clustering in Process Mining. In: Ardagna, D., Mecella, M., Yang, J. (eds.) BPM 2008 Workshops. LNBIP, vol. 17, pp. 109–120. Springer, Heidelberg (2009)
Greco, G., Guzzo, A., Pontieri, L., Sacca, D.: Disco-covering Expressive Process Models by Clusering Log Traces. IEEE Trans. Knowl. Data Eng., 1010–1027 (2006)
Jagadeesh Chandra Bose, R.P., van der Aalst, W.M.P.: Context Aware Trace Clustering: Towards Improving Process Mining Results. In: Proceedings of the SIAM International Conference on Data Mining, SDM, pp. 401–412 (2009)
Song, M., Günther, C.W., van der Aalst, W.M.P.: Trace Clustering in Process Mining. In: Ardagna, D., Mecella, M., Yang, J. (eds.) BPM 2008 Workshops. LNBIP, vol. 17, pp. 109–120. Springer, Heidelberg (2009)
Bose, R.P.J.C., van der Aalst, W.M.P.: Trace Clustering Based on Conserved Patterns: Towards Achieving Better Process Models. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009 Workshops. LNBIP, vol. 43, pp. 170–181. Springer, Heidelberg (2010)
Hammouda, K.M., Kamel, M.S.: Efficient phrase-based document indexing for web document clustering. IEEE Transactions on Knowledge and Data Engineering 16(10), 1279–1296 (2004)
Zamir, O., Etzioni, O.: Web document clustering: a feasibility demonstration. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 46–54 (1998)
Wen, L., van der Aalst, W.M.P., Wang, J., Sun, J.: Mining Process Models with Non-Free Choice Constructs. Data Min. Knowl. Discov. 15(2), 145–182 (2007)
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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Wang, X., Zhang, L., Cai, H. (2012). Using Suffix-Tree to Identify Patterns and Cluster Traces from Event Log. In: Das, V.V., Ariwa, E., Rahayu, S.B. (eds) Signal Processing and Information Technology. SPIT 2011. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 62. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32573-1_20
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DOI: https://doi.org/10.1007/978-3-642-32573-1_20
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