Szczegóły publikacji
Opis bibliograficzny
Approximate clustering of noisy biomedical data / Krzysztof BORYCZKO, Marcin KURDZIEL // W: Computational Science – ICCS 2008 : 8th International Conference : Kraków, Poland, June 23–25, 2008 : proceedings, Pt. 1 / eds. Marian Bubak, Geert Dick van Albada, Jack Dongarra, Peter M. A. Sloot. — Berlin ; Heidelberg : Springer-Verlag, cop. 2008 + CD-ROM. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 5101). — Dołączony CD-ROM zawiera pełne teksty referatów z pt. 1, pt. 2, pt. 3. — ISBN: 978-3-540-69383-3; ISBN: 3-540-69383-1; e-ISBN: 978-3-540-69384-0. — S. 630–640. — Bibliogr. s. 639–640, Abstr.
Autorzy (2)
Dane bibliometryczne
| ID BaDAP | 40261 |
|---|---|
| Data dodania do BaDAP | 2008-09-25 |
| Rok publikacji | 2008 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Czasopisma/serie | Theoretical Computer Science and General Issues, Lecture Notes in Computer Science |
Abstract
Classical clustering algorithms often perform poorly on data harboring background noise, i.e. large number of observations distributed uniformly in the feature space. Here, we present a new density-based algorithm for approximate clustering of such noisy data. The algorithm employs Shared Nearest Neighbor Graphs for estimating local data density and identification of core points, which are assumed to indicate locations of clusters. Partitioning of core points into clusters is performed by means of Mutual Nearest Neighbor distance measure. This similarity measure is sensitive to changes in local data density, and is thus useful for discovering clusters that differ in this respect. Performance of the presented algorithm was demonstrated on three data sets, two synthetic and one real world. In all cases, meaningful clustering structures were discovered.