Abstract
Cluster discovery from highly-dimensional data is a challenging task, that has been studied for years in the fields of data mining and machine learning. Most of them focus on automation of the process, resulting in the clusters that once discovered have to be carefully analyzed to assign semantics for numerical labels. However, it is often the case that such an explicit, symbolic knowledge about possible clusters is available prior to clustering and can be used to enhance the learning process. More importantly, we demonstrate how a machine learning model can be used to refine the expert knowledge and extend it with an aid of explainable AI algorithms. We present our framework on an artificial, reproducible dataset.
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See the project webpage at http://PACMEL.geist.re.
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Acknowledgements
The paper is funded from the PACMEL project funded by the National Science Centre, Poland under CHIST-ERA programme (NCN 2018/27/Z/ST6/03392). The authors are grateful to ACK Cyfronet, Krakow for granting access to the computing infrastructure built in the projects No. POIG.02.03.00-00-028/08 “PLATON - Science Services Platform” and No. POIG.02.03.00-00-110/13 “Deploying high-availability, critical services in Metropolitan Area Networks (MAN-HA)”.
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Bobek, S., Nalepa, G.J. (2021). Augmenting Automatic Clustering with Expert Knowledge and Explanations. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12745. Springer, Cham. https://doi.org/10.1007/978-3-030-77970-2_48
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DOI: https://doi.org/10.1007/978-3-030-77970-2_48
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