About: Hierarchical Cluster Analysis – Various Approaches to Data Preparation

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Hierarchical Cluster Analysis – Various Approaches to Data Preparation Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	The article deals with two various approaches to data preparation to avoid multicollinearity. The aim of the article is to find similarities among the e-communication level of EU states using hierarchical cluster analysis. The original set of fourteen indicators was first reduced on the basis of correlation analysis while in case of high correlation indicator of higher variability was included in further analysis. Secondly the data were transformed using principal component analysis while the principal components are poorly correlated. For further analysis five principal components explaining about 92% of variance were selected. Hierarchical cluster analysis was performed both based on the reduced data set and the principal component scores. Both times three clusters were assumed following Pseudo t-Squared and Pseudo F Statistic, but the final clusters were not identical. An important characteristic to compare the two results found was to look at the proportion of variance accounted for by the cluste The article deals with two various approaches to data preparation to avoid multicollinearity. The aim of the article is to find similarities among the e-communication level of EU states using hierarchical cluster analysis. The original set of fourteen indicators was first reduced on the basis of correlation analysis while in case of high correlation indicator of higher variability was included in further analysis. Secondly the data were transformed using principal component analysis while the principal components are poorly correlated. For further analysis five principal components explaining about 92% of variance were selected. Hierarchical cluster analysis was performed both based on the reduced data set and the principal component scores. Both times three clusters were assumed following Pseudo t-Squared and Pseudo F Statistic, but the final clusters were not identical. An important characteristic to compare the two results found was to look at the proportion of variance accounted for by the cluste (en)
Title	Hierarchical Cluster Analysis – Various Approaches to Data Preparation Hierarchical Cluster Analysis – Various Approaches to Data Preparation (en)
skos:prefLabel	Hierarchical Cluster Analysis – Various Approaches to Data Preparation Hierarchical Cluster Analysis – Various Approaches to Data Preparation (en)
skos:notation	RIV/60460709:41110/13:61432!RIV14-MSM-41110___
http://linked.open...avai/riv/aktivita	S
http://linked.open...avai/riv/aktivity	S
http://linked.open...iv/cisloPeriodika	3
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Poláčková, Julie Pacáková, Zuzana
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Česká zemědělská univerzita v Praze / Provozně ekonomická fakulta
http://linked.open...dnocenehoVysledku	77309
http://linked.open...ai/riv/idVysledku	RIV/60460709:41110/13:61432
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Hierarchical clustering, PCA, correlation, Pseudo t2, Pseudo F Statistic, e-communication, Internet satisfaction (en)
http://linked.open.../riv/klicoveSlovo	PCA correlation Hierarchical clustering Internet satisfaction Pseudo F Statistic Pseudo t2 e-communication
http://linked.open...odStatuVydavatele	CZ - Česká republika
http://linked.open...ontrolniKodProRIV	[C5F657E997E1]
http://linked.open...i/riv/nazevZdroje	AGRIS on-line Papers in Economics and Informatics
http://linked.open...in/vavai/riv/obor	BB
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...UplatneniVysledku	2013
http://linked.open...v/svazekPeriodika	V
http://linked.open...iv/tvurceVysledku	Pacáková, Zuzana Poláčková, Julie
http://linked.open...ain/vavai/riv/wos	0
issn	1804-1930
number of pages	11 (xsd:int)
http://localhost/t...ganizacniJednotka	41110

Faceted Search & Find service v1.16.118 as of Jun 21 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 58 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software