Analysis of Mobile Social Networks Using Clustering

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F16%3A50004169" target="_blank" >RIV/62690094:18450/16:50004169 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216275:25530/17:39912024

Výsledek na webu

<a href="http://dx.doi.org/10.1166/asl.2016.6688" target="_blank" >http://dx.doi.org/10.1166/asl.2016.6688</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1166/asl.2016.6688" target="_blank" >10.1166/asl.2016.6688</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Analysis of Mobile Social Networks Using Clustering

Popis výsledku v původním jazyce

Social networks are becoming a phenomenon of the 21st century. As well as gaining new users on the Internet, they also began to penetrate into mobile devices. The aim of the paper is to introduce the five most commonly used mobile social networks in Europe, with a focus on the European Union and their analysis. For this purpose, the data-mining technology of K-means for clustering process will be used. Prior to the cluster analysis itself, the data will be illustrated using principal component method for graphical visualization, which will define the three main components. The factors according to which there is a clustering, will be detected by a factor analysis using "varimax simple" rotation. In the last part of the paper, the 28 European Union countries are divided into four homogeneous groups, i.e. clusters, for each of the years 2010-2014. These are average values representing the states contained in the given cluster, in comparison with data for all social networks for the European Union and Europe. The input assumption that data (criteria) that enter the cluster analysis are not affected by multi-collinearity will be verified by Spearman correlation coefficient.

Název v anglickém jazyce

Analysis of Mobile Social Networks Using Clustering

Popis výsledku anglicky

Social networks are becoming a phenomenon of the 21st century. As well as gaining new users on the Internet, they also began to penetrate into mobile devices. The aim of the paper is to introduce the five most commonly used mobile social networks in Europe, with a focus on the European Union and their analysis. For this purpose, the data-mining technology of K-means for clustering process will be used. Prior to the cluster analysis itself, the data will be illustrated using principal component method for graphical visualization, which will define the three main components. The factors according to which there is a clustering, will be detected by a factor analysis using "varimax simple" rotation. In the last part of the paper, the 28 European Union countries are divided into four homogeneous groups, i.e. clusters, for each of the years 2010-2014. These are average values representing the states contained in the given cluster, in comparison with data for all social networks for the European Union and Europe. The input assumption that data (criteria) that enter the cluster analysis are not affected by multi-collinearity will be verified by Spearman correlation coefficient.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Advanced science letters

ISSN

1936-6612

e-ISSN

—

Svazek periodika

Volume 22,

Číslo periodika v rámci svazku

5-6

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

5

Strana od-do

1273-"1277(5)"

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-84985982537