Hierarchical clustering with concave data sets

Matej Francetič (Avtor), Mateja Nagode (Avtor), Bojan Nastav (Avtor)

Povzetek

Clustering methods are among the most widely used methods in multivariate analysis. Two main groups of clustering methods can be distinguished: hierarchical and non-hierarchical. Due to the nature of the problem examined, this paper focuses on hierarchical methods such as the nearest neighbour, the furthest neighbour, Ward's method, between-groups linkage, within-groups linkage, centroid and median clustering. The goal is to assess the performanceof different clustering methods when using concave sets of data, and also to figure out in which types of different data structures can these methods reveal and correctly assign group membership. The simulations were runin a two- and three-dimensional space. Using different standard deviations of points around the skeleton further modified each of the two original shapes. In this manner various shapes of sets with different inter-cluster distances were generated. Generating the data sets provides the essential knowledge of cluster membership for comparing the clustering methods? performances. Conclusions are important and interesting since real life data seldom follow the simple convex-shaped structure, but need further work, such as the bootstrap application, the inclusion of the dendrogram-based analysis or other data structures. Therefore this paper can serve as a basis for further study of hierarchical clustering performance with concave sets.

Ključne besede

Razvrščanje v skupine;Multivariatna analiza;

Podatki

Jezik:	Angleški jezik
Leto izida:	2005
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	UL FDV - Fakulteta za družbene vede
Založnik:	Fakulteta za družbene vede
UDK:	303
COBISS:	24314717
ISSN:	1854-0023
Št. ogledov:	913
Št. prenosov:	193
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

Sekundarni jezik:	Neznan jezik
Sekundarne ključne besede:	Cluster analysis;Multivariate analysis;
URN:	URN:NBN:SI
Vrsta dela (COBISS):	Delo ni kategorizirano
Strani:	str. 173-193
Letnik:	ǂVol. ǂ2
Zvezek:	ǂno. ǂ2
Čas izdaje:	2005
ID:	1743917