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Authors: Michael Behringer ; Pascal Hirmer ; Dennis Tschechlov and Bernhard Mitschang

Affiliation: Institute of Parallel and Distributed Systems, University of Stuttgart, Universitätsstr. 38, 70569 Stuttgart, Germany

Keyword(s): Clustering, Explainability, Human-in-the-Loop.

Abstract: Today, the amount of data is growing rapidly, which makes it nearly impossible for human analysts to comprehend the data or to extract any knowledge from it. To cope with this, as part of the knowledge discovery process, many different data mining and machine learning techniques were developed in the past. A famous representative of such techniques is clustering, which allows the identification of different groups of data (the clusters) based on data characteristics. These algorithms need no prior knowledge or configuration, which makes them easy to use, but interpreting and explaining the results can become very difficult for domain experts. Even though different kinds of visualizations for clustering results exist, they do not offer enough details for explaining how the algorithms reached their results. In this paper, we propose a new approach to increase explainability for clustering algorithms. Our approach identifies and selects features that are most meaningful for the clusteri ng result. We conducted a comprehensive evaluation in which, based on 216 synthetic datasets, we first examined various dispersion metrics regarding their suitability to identify meaningful features and we evaluated the achieved precision with respect to different data characteristics. This evaluation shows, that our approach outperforms existing algorithms in 93 percent of the examined datasets. (More)

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Paper citation in several formats:
Behringer, M.; Hirmer, P.; Tschechlov, D. and Mitschang, B. (2022). Increasing Explainability of Clustering Results for Domain Experts by Identifying Meaningful Features. In Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-758-569-2; ISSN 2184-4992, SciTePress, pages 364-373. DOI: 10.5220/0011092000003179

@conference{iceis22,
author={Michael Behringer. and Pascal Hirmer. and Dennis Tschechlov. and Bernhard Mitschang.},
title={Increasing Explainability of Clustering Results for Domain Experts by Identifying Meaningful Features},
booktitle={Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2022},
pages={364-373},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011092000003179},
isbn={978-989-758-569-2},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - Increasing Explainability of Clustering Results for Domain Experts by Identifying Meaningful Features
SN - 978-989-758-569-2
IS - 2184-4992
AU - Behringer, M.
AU - Hirmer, P.
AU - Tschechlov, D.
AU - Mitschang, B.
PY - 2022
SP - 364
EP - 373
DO - 10.5220/0011092000003179
PB - SciTePress