Abstract
In process mining, trace clustering is an important technique that at-tracts the attention of researchers to solve the large and complex volume of event logs. Traditional trace clustering often uses available data mining algorithms which do not exploit the characteristic of processes. In this study, we propose a new trace clustering algorithm, especially for the process mining, based on the using trace context. The proposed clustering algorithm can automatic detects the number of clusters, and it does not need a convergence iteration like traditional ones like K-means. The algorithm takes two loops over the input to generate the clusters, thus the complexity is greatly reduced. Experimental results show that our method also has good results when compared to traditional methods.
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Bui, HN., Nguyen, TT., Nguyen, TC., Ha, QT. (2018). A New Trace Clustering Algorithm Based on Context in Process Mining. In: Nguyen, H., Ha, QT., Li, T., Przybyła-Kasperek, M. (eds) Rough Sets. IJCRS 2018. Lecture Notes in Computer Science(), vol 11103. Springer, Cham. https://doi.org/10.1007/978-3-319-99368-3_50
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DOI: https://doi.org/10.1007/978-3-319-99368-3_50
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