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Interpretable Data Partitioning Through Tree-Based Clustering Methods

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Discovery Science (DS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14276))

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Abstract

The growing interpretable machine learning research field is mainly focusing on the explanation of supervised approaches. However, also unsupervised approaches might benefit from considering interpretability aspects. While existing clustering methods only provide the assignment of records to clusters without justifying the partitioning, we propose tree-based clustering methods that offer interpretable data partitioning through a shallow decision tree. These decision trees enable easy-to-understand explanations of cluster assignments through short and understandable split conditions. The proposed methods are evaluated through experiments on synthetic and real datasets and proved to be more effective than traditional clustering approaches and interpretable ones in terms of standard evaluation measures and runtime. Finally, a case study involving human participation demonstrates the effectiveness of the interpretable clustering trees returned by the proposed method.

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Notes

  1. 1.

    After a preliminary experimentation we discarded evaluation measures for regressors as they do not consider the separation of the data but the performance of the regressor.

  2. 2.

    https://github.com/cri98li/ParTree.

  3. 3.

    https://github.com/deric/clustering-benchmark, https://archive.ics.uci.edu/ml/datasets.php, https://www.kaggle.com/datasets.

  4. 4.

    https://scikit-learn.org/stable/index.html, https://github.com/annoviko/pyclustering/, https://github.com/nicodv/kmodes.

  5. 5.

    Details for the parameter values tested are available on the repository. However, since the objective is towards interpretable clustering, we do not search for more than 12 clusters or trees deeper than 10. Also, we remark that it is outside the purpose of this study to design strategies to identify good values for \({max\_clusters}, {max\_depth}, {min\_sample}, \varepsilon \). We leave this task for a future study.

  6. 6.

    Similar results to those reported are obtained with best parameters w.r.t other measures.

  7. 7.

    https://www.qualtrics.com/.

  8. 8.

    All participants provided written informed consent and received no monetary rewards.

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Acknowledgment

This work is partially supported by the EU NextGenerationEU programme under the funding schemes PNRR-PE-AI FAIR (Future Artificial Intelligence Research), PNRR-SoBigData.it - Strengthening the Italian RI for Social Mining and Big Data Analytics - Prot. IR0000013, H2020-INFRAIA-2019-1: Res. Infr. G.A. 871042 SoBigData++, G.A. 761758 Humane AI, G.A. 952215 TAILOR, ERC-2018-ADG G.A. 834756 XAI, G.A. 101070416 Green.Dat.AI and CHIST-ERA-19-XAI-010 SAI.

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Authors and Affiliations

  1. University of Pisa, Pisa, Italy

    Riccardo Guidotti & Cristiano Landi

  2. ISTI-CNR Pisa, Pisa, Italy

    Riccardo Guidotti, Andrea Beretta, Daniele Fadda & Mirco Nanni

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  1. Riccardo Guidotti
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  2. Cristiano Landi
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  3. Andrea Beretta
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  4. Daniele Fadda
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  5. Mirco Nanni
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Correspondence to Riccardo Guidotti .

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Editors and Affiliations

  1. Waikato University, Hamilton, New Zealand

    Albert Bifet

  2. Aeronautics Institute of Technology, São José dos Campos, Brazil

    Ana Carolina Lorena

  3. University of Porto, Porto, Portugal

    Rita P. Ribeiro

  4. University of Porto, Porto, Portugal

    João Gama

  5. University of Coimbra, Coimbra, Portugal

    Pedro H. Abreu

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Guidotti, R., Landi, C., Beretta, A., Fadda, D., Nanni, M. (2023). Interpretable Data Partitioning Through Tree-Based Clustering Methods. In: Bifet, A., Lorena, A.C., Ribeiro, R.P., Gama, J., Abreu, P.H. (eds) Discovery Science. DS 2023. Lecture Notes in Computer Science(), vol 14276. Springer, Cham. https://doi.org/10.1007/978-3-031-45275-8_33

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  • DOI: https://doi.org/10.1007/978-3-031-45275-8_33

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