W. M. Van-der-aalst, Process Mining: Discovery, Conformance and Enhancement of Business Processes, 2011.

R. P. Bose and W. M. Van-der-aalst, Context Aware Trace Clustering: Towards Improving Process Mining Results, Proceedings of the SIAM International Conference on Data Mining, pp.401-412, 2009.
DOI : 10.1137/1.9781611972795.35

R. P. Bose and W. M. Van-der-aalst, Trace Clustering Based on Conserved Patterns: Towards Achieving Better Process Models, Business Process Management Workshops, pp.170-181, 2010.
DOI : 10.1007/978-3-642-12186-9_16
URL : http://tmpmining.win.tue.nl/_media/publications/jcbose2009c.pdf

A. Burattin, M. Cimitile, F. M. Maggi, and A. Sperduti, Online Discovery of Declarative Process Models from Event Streams, IEEE Transactions on Services Computing, vol.8, issue.6, pp.833-846, 2015.
DOI : 10.1109/TSC.2015.2459703

J. Carmona and R. Gavalda, Online Techniques for Dealing with Concept Drift in Process Mining, Advances in Intelligent Data Analysis XI, pp.90-102, 2012.
DOI : 10.1007/978-3-642-34156-4_10
URL : http://www.lsi.upc.edu/%7Egavalda/ida2012.pdf

J. Gama, Knowledge discovery from data streams, 2010.
DOI : 10.1201/ebk1439826119

S. Goedertier, J. De-weerdt, D. Martens, J. Vanthienen, and B. Baesens, Process discovery in event logs: An application in the telecom industry, Applied Soft Computing, vol.11, issue.2, pp.1697-1710, 2011.
DOI : 10.1016/j.asoc.2010.04.025

B. F. Hompes, J. C. Buijs, W. M. Van-der-aalst, P. M. Dixit, and J. Buurman, Discovering Deviating Cases and Process Variants Using Trace Clustering, Proceedings of the 27th Benelux Conference on Artificial Intelligence (BNAIC), p.2015, 2006.

D. Luengo and M. Sepúlveda, Applying Clustering in Process Mining to Find Different Versions of a Business Process That Changes over Time, Business Process Management Workshops, pp.153-158, 2012.
DOI : 10.1007/978-3-642-12186-9_16

A. Maaradji, M. Dumas, M. L. Rosa, and A. Ostovar, Fast and Accurate Business Process Drift Detection, Business Process Management, pp.406-422, 2015.
DOI : 10.1007/978-3-319-23063-4_27

J. Martjushev, R. P. Bose, and W. M. Van-der-aalst, Change Point Detection and Dealing with Gradual and Multi-order Dynamics in Process Mining, Perspectives in Business Informatics Research, pp.161-178, 2015.
DOI : 10.1007/978-3-319-21915-8_11

T. Thaler, S. F. Ternis, P. Fettke, and P. Loos, A Comparative Analysis of Process Instance Cluster Techniques, Proceedings of the 12th International Conference on Wirtschaftsinformatik. Internationale Tagung Wirtschaftsinformatik (WI-15), p.2015, 2005.

B. F. Van-dongen, Real-life Event Logs -Hospital Log, pp.9769-9772, 2011.

S. Van-dongen, A Cluster Algorithm for Graphs, National Research Institute for Mathematics and Computer Science in the Netherlands, 2000.

G. M. Veiga and D. R. Ferreira, Understanding Spaghetti Models with Sequence Clustering for ProM, Business Process Management Workshops, pp.92-103, 2010.
DOI : 10.1007/978-3-642-12186-9_10
URL : http://web.ist.utl.pt/diogo.ferreira/papers/veiga10understanding.pdf

P. Weber, B. Bordbar, and P. Tino, Real-Time Detection of Process Change using Process Mining, Imperial College Computing Student Workshop, pp.108-114, 2011.

J. , D. Weerdt, and S. K. Vanden-broucke, SECPI: Searching for Explanations for Clustered Process Instances, Business Process Management, pp.408-415, 2014.