Comprehensive Differentiation of Partitional Clusterings

Lars Schütz, Lars Schütz, Korinna Bade, Andreas Nürnberger

2023

Abstract

Clustering data is a major task in machine learning. From a user’s perspective, one particular challenge in this area is the differentiation of at least two clusterings. This is especially true when users have to compare clusterings down to the smallest detail. In this paper, we focus on the identification of such clustering differences. We propose a novel clustering difference model for partitional clusterings. It allows the computational detection of differences between partitional clusterings by keeping a full description of changes in input, output, and model parameters. For this purpose, we also introduce a complete and flexible partitional clustering representation. Both the partitional clustering representation and the partitional clustering difference model can be applied to unsupervised and semi-supervised learning scenarios. Finally, we demonstrate the usefulness of the proposed partitional clustering difference model through its application to real-world use cases in planning and decision processes of the e-participation domain.

Paper Citation

in Harvard Style

Schütz L., Bade K. and Nürnberger A. (2023). Comprehensive Differentiation of Partitional Clusterings. In Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-648-4, SciTePress, pages 243-255. DOI: 10.5220/0011762000003467

in Bibtex Style

@conference{iceis23,
author={Lars Schütz and Korinna Bade and Andreas Nürnberger},
title={Comprehensive Differentiation of Partitional Clusterings},
booktitle={Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2023},
pages={243-255},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011762000003467},
isbn={978-989-758-648-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Comprehensive Differentiation of Partitional Clusterings
SN - 978-989-758-648-4
AU - Schütz L.
AU - Bade K.
AU - Nürnberger A.
PY - 2023
SP - 243
EP - 255
DO - 10.5220/0011762000003467
PB - SciTePress