Szczegóły publikacji
Opis bibliograficzny
Augmenting automatic clustering with expert knowledge and explanations / Szymon BOBEK, Grzegorz J. NALEPA // W: Computational Science – ICCS 2021 : 21st international conference : Krakow, Poland, June 16–18, 2021 : proceedings, Pt. 4 / eds. Maciej Paszyński, [et al.]. — Cham : Springer Nature Switzerland, cop. 2021. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 12745. Theoretical Computer Science and General Issues ; ISSN 0302-9743). — ISBN: 978-3-030-77969-6; e-ISBN: 978-3-030-77970-2. — S. 631–638. — Bibliogr., Abstr. — Publikacja dostępna online od: 2021-06-09. — Sz. Bobek, G. J. Nalepa - pierwsza afiliacja: Jagiellonian University
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 134758 |
|---|---|
| Data dodania do BaDAP | 2021-06-28 |
| DOI | 10.1007/978-3-030-77970-2_48 |
| Rok publikacji | 2021 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Computational Science 2021 |
| Czasopisma/serie | Lecture Notes in Computer Science, Theoretical Computer Science and General Issues |
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.
