Szczegóły publikacji
Opis bibliograficzny
Explainable clustering with multidimensional bounding boxes / Michał KUK, Szymon Bobek, Grzegorz J. Nalepa // W: DSAA'2021 [Dokument elektroniczny] : 8th international conference on Data Science and Advanced Analytics : 6–9 October 2021, Porto, Portugal : proceedings. — Wersja do Windows. — Dane tekstowe. — [Piscataway] : IEEE, cop. 2021. — e-ISBN: 978-1-6654-2099-0. — S. [1–10]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [10], Abstr. — Publikacja dostępna online od: 2021-10-20
Autorzy (3)
- AGHKuk Michał
- Bobek Szymon
- Nalepa Grzegorz
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 139117 |
|---|---|
| Data dodania do BaDAP | 2022-02-15 |
| Tekst źródłowy | URL |
| DOI | 10.1109/DSAA53316.2021.9564220 |
| Rok publikacji | 2021 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | IEEE International Conference on Data Science and Advanced Analytics 2021 |
Abstract
Explainable Artificial Intelligence (XAI) aims at introducing transparency and intelligibility into decision-making process of AI systems. Most of the work in this area is focused on supervised machine learning tasks such as classification and regression. Unsupervised algorithms such as clustering can also be explained with existing approaches. This is most often achieved by explaining a classifier trained on cluster data with cluster labels as a dependant variable. However, with such a transformation the information about cluster shape and distribution is lost, which may lead to wrong interpretation of explanations. In this paper, we introduce a method that aids end experts in cluster analysis with human-readable rule-based explanations. We use state-of-the-art explanation mechanism on the multidimensional bounding boxes that represent arbitrarily-shaped clusters. We demonstrate our approach on reproducible synthetic datasets.
