Katarina Košmelj (Avtor), Lynne Billard (Avtor)

Povzetek

Mallowsʼ L2 distance allows for decomposition of total inertia into within and between inertia according to Huygens theorem. It can be decomposed into three terms: the location term, the spread term and the shape term; a simple and straightforward proof of this theorem is presented. These characteristics are very helpful in the interpretation of the results for some distance-based methods, such as clustering by k-means and classical multidimensional scaling. For histogram-type data, Mallowsʼ L2 distance is preferable because its calculation is simple, even when the number and length of the histogramsʼ subintervals differ. An illustration of its use on population pyramids for 14 East European countries in the period 1995-2015 is presented. The results provide an insight into the information that this distance can extract from a complex dataset.

Ključne besede

statistične metode;statistika;klaster analiza;Mallows L2 razdalja;večrazsežnostno lestvičenje;MDS;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL BF - Biotehniška fakulteta
UDK: 303
COBISS: 7389561 Povezava se bo odprla v novem oknu
ISSN: 1854-0023
Št. ogledov: 651
Št. prenosov: 147
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Angleški jezik
URN: URN:NBN:SI
Vrsta dela (COBISS): Delo ni kategorizirano
Strani: str. 107-118
Letnik: ǂVol. ǂ9
Zvezek: ǂno. ǂ2
Čas izdaje: 2012
ID: 1466985