Szczegóły publikacji
Opis bibliograficzny
Clustering based on the Krill Herd Algorithm with selected validity measures / Piotr Andrzej KOWALSKI, Szymon ŁUKASIK, Małgorzata Charytanowicz, Piotr KULCZYCKI // W: FedCSIS : abstracts of the Federated Conference on Computer Science and Information Systems : 11–14 September, 2016, Gdansk, Poland : book of abstracts. — [Poland : s. n.], [2016]. — ISBN: 978-836081090-3. — s. 30. — Pełny tekst W: FedCSiS 2016 [Dokument elektroniczny] : preproceedings of the 2016 Federated Conference on Computer Science and Information Systems : September 11–14, 2016, Gdańsk, Poland / eds. Maria Ganzha, Leszek Maciaszek, Marcin Paprzycki. — Wersja do Windows. — Dane tekstowe. — Warsaw : Polskie Towarzystwo Informatyczne, cop. 2016. — (Annals of Computer Science and Information Systems ; ISSN 2300-5963 ; vol. 8). — ISBN 978-83-60810-90-3. — S. 79–87. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://fedcsis.org/proceedings/2016/pliks/fedcsis.pdf [2016-10-04]. — Bibliogr. s. 86–87, Abstr. — Afiliacja Autorów zamieszczona przy pełnym tekście. — Dod. afiliacja P. A. Kowalski, S. Łukasik, P. Kulczycki: Polish Academy of Sciences
Autorzy (4)
- AGHKowalski Piotr Andrzej
- AGHŁukasik Szymon
- Charytanowicz Małgorzata
- AGHKulczycki Piotr
Dane bibliometryczne
| ID BaDAP | 101171 |
|---|---|
| Data dodania do BaDAP | 2016-10-04 |
| DOI | 10.15439/2016F295 |
| Rok publikacji | 2016 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Konferencja | 2016 Federated Conference on Computer Science and Information Systems |
Abstract
This paper describes a new approach to metaheuristic-based data clustering by means of Krill Herd Algorithm (KHA). In this work, KHA is used to find centres of the cluster groups. Moreover, the number of clusters is set up at the beginning of the procedure, and during the subsequent iterations of the optimization algorithm, particular solutions are evaluated by selected validity criteria. The proposed clustering algorithm has been numerically verified using twelve data sets taken from the UCI Machine Learning Repository. Additionally, all cases of clustering were compared with the most popular method of k-means, through the Rand Index being applied as a validity measure. © 2016 Polish Information Processing Society.