Katarina Košmelj (Author), Lynne Billard (Author)

Abstract

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.

Keywords

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

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UL BF - Biotechnical Faculty
UDC: 303
COBISS: 7389561 Link will open in a new window
ISSN: 1854-0023
Views: 651
Downloads: 147
Average score: 0 (0 votes)
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Other data

Secondary language: English
URN: URN:NBN:SI
Type (COBISS): Not categorized
Pages: str. 107-118
Volume: ǂVol. ǂ9
Issue: ǂno. ǂ2
Chronology: 2012
ID: 1466985