@inproceedings{72b612c215794071b3061fc0d57e73a9,
title = "A novel trace clustering technique based on constrained trace alignment",
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.",
keywords = "Business process management, Constrained trace alignment, Constrained trace clustering, Process mining, Trace clustering",
author = "Pan Wang and Wen{\textquoteright}an Tan and Anqiong Tang and Kai Hu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2018.; 3rd International Conference on Human Centered Computing, HCC 2017 ; Conference date: 07-08-2017 Through 09-08-2017",
year = "2018",
doi = "10.1007/978-3-319-74521-3\_7",
language = "英语",
isbn = "9783319745206",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "53--63",
editor = "Bo Hu and Qiaohong Zu",
booktitle = "Human Centered Computing - 3rd International Conference, HCC 2017, Revised Selected Papers",
address = "德国",
}