Szczegóły publikacji
Opis bibliograficzny
Causal rules detection in streams of unlabeled, mixed type values with finit domains / Szymon BOBEK, Kamil Jurek // W: Intelligent Data Engineering and Automated Learning – IDEAL 2018 : 19th international conference : Madrid, Spain, November 21–23, 2018 : proceedings, Pt. 2 / eds. Hujun Yin, [et al.]. — Cham: Springer Nature Switzerland AG, cop. 2018. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; 11315). — ISBN: 978-3-030-03495-5; e-ISBN: 978-3-030-03496-2. — S. 64–74. — Bibliogr. s. 73–74, Abstr.
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 118623 |
|---|---|
| Data dodania do BaDAP | 2019-01-03 |
| Tekst źródłowy | URL |
| DOI | 10.1007/978-3-030-03496-2_8 |
| Rok publikacji | 2018 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Intelligent Data Engineering and Automated Learning 2018 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Knowledge discovery from data streams in recent years become one of the most important research area in a domain of data science. This is mainly due to the rapid development of mobile devices, and Internet of things solutions which allow for obtaining petabytes of data within minutes. All of the modern approaches either use representation that is flat in time domain, or follow black-box model paradigm. This reduces the expressiveness of models and limits the intelligibility of the system. In this paper we present an algorithm for rule discovery that allows to capture temporal causalities between numeric and symbolic attributes.
