Abstract
Whenever traditional process discovery techniques are confronted with complex and flexible environments, equipping all the traces with just one single model might lead to a spaghetti-like process description. Trace clustering which splits the logs into clusters and applies discovery algorithm per cluster has affirmed to be a versatile solution for that. Nevertheless, most trace clustering techniques are not precise enough due to the indiscriminate treatment on the activities captured in traces. As a result, the impacts of some important activities are reduced and some typical information may be distorted or even lost during comparison. In this paper, we propose a novel trace clustering technique that based on constrained traces alignment and then adapt two appropriate clustering strategies into process mining perspective. And experiments on real-life event logs show that our technique has compelling outperformance in terms of process models complexity and comprehensibility.
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Acknowledgment
This work is supported in part by the National Natural Science Foundation of China under Grant No. 61672022, Key Disciplines of Computer Science and Technology of Shanghai Polytechnic University under Grant No. XXKZD1604, the Fundamental Research Funds for the Central Universities and Foundation of Graduate Innovation of Shanghai Polytechnic University, and Foundation of Graduate Innovation Center in NUAA under Grant No. kfjj20161601.
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Wang, P., Tan, W., Tang, A., Hu, K. (2018). A Novel Trace Clustering Technique Based on Constrained Trace Alignment. In: Zu, Q., Hu, B. (eds) Human Centered Computing. HCC 2017. Lecture Notes in Computer Science(), vol 10745. Springer, Cham. https://doi.org/10.1007/978-3-319-74521-3_7
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