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Wyświetlanie 1-4 z 4
Tytuł:
Analysis of innovations in the European Union via ensemble symbolic density clustering
Analiza innowacyjności krajów Unii Europejskiej z zastosowaniem wielomodelowej klasyfikacji gęstościowej danych symbolicznych
Autorzy:
Pełka, Marcin
Powiązania:
https://bibliotekanauki.pl/articles/425070.pdf
Data publikacji:
2018
Wydawca:
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Tematy:
innovations
European Union
symbolic data analysis
ensemble clustering
Opis:
Innovations play a very important role in the modern economy. They are the key to a higher quality of life, better jobs and economy and sustainable development. The innovation policy is a key element of both national and European Union strategy. The main aim of this paper is to present an ensemble clustering of European Union countries (member states) considering their innovativeness. In the empirical section, symbolic density-based ensemble clustering is used to obtain the co-occurrence matrix. The paper uses symbolicDA, clusterSim and dbscan packages of R software for all calculations. Four different clusters where obtained in the result of clustering. Cluster 1 contains highinnovative countries (innovation leaders). This cluster is also the least homogenous. Cluster 2 contains post-communist countries mainly from central Europe. These countries can be seen as rather mid-low innovative (they try to “catch up” with innovation leaders). Cluster 3 contains moderate innovators. Cluster 4 contains two countries that are also mid-innovative.
Źródło:
Econometrics. Ekonometria. Advances in Applied Data Analytics; 2018, 22, 3; 84-98
1507-3866
Pojawia się w:
Econometrics. Ekonometria. Advances in Applied Data Analytics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Podejście wielomodelowe analizy danych symbolicznych w ocenie pozycji produktów na rynku
Ensemble learning for symbolic datain product positioning
Autorzy:
Pełka, Marcin
Powiązania:
https://bibliotekanauki.pl/articles/424929.pdf
Data publikacji:
2013
Wydawca:
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Tematy:
ensemble clustering
cluster analysis of symbolic data
product positioning
Opis:
Product positioning is a wide range of business activities. Positioning is the process by which marketers try to create an image or identity in the minds of their target market for its product, brand, or organization. The main aim of the paper is to preset and apply ensemble learning for symbolic data in cluster analysis in order to evaluate a product position. Empirical part of the paper presents the application of co-occurrence matrix and bagging algorithm in ensemble learning for symbolic data (car market data was used). These two approaches reached almost the same results when considering adjusted Rand index.
Źródło:
Econometrics. Ekonometria. Advances in Applied Data Analytics; 2013, 2(40); 95-102
1507-3866
Pojawia się w:
Econometrics. Ekonometria. Advances in Applied Data Analytics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analysis of happiness in EU countries using the multi-model classification based on models of symbolic data
Autorzy:
Pełka, Marcin
Powiązania:
https://bibliotekanauki.pl/articles/425036.pdf
Data publikacji:
2019
Wydawca:
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Tematy:
happiness
the European Union
symbolic data analysis
ensemble clustering
Opis:
The results of happiness analysis are presented in the form of a World Happiness Report that covers 156 countries and 17 different indicators. In the article model-based clustering ensemble is built to determine what selected European countries have similar patterns of happiness. The results are analyzed using multidimensional scaling and a decision tree to find out what factors determine cluster memberships. In the empirical part, three clusters were detected The first contains countries: Austria, Denmark, Finland, Germany, Ireland, Luxembourg, the Netherlands, Norway, Sweden, Switzerland and the United Kingdom. They have the highest values for all the variables, except the negative affect. The second cluster contains seven countries: Bulgaria, Estonia, Hungary, Lithuania, Poland, Romania and Slovakia. This cluster is also the most homogeneous one. The third cluster contains eight countries: Cyprus, the Czech Republic, France, Greece, Italy, Portugal, Slovenia and Spain.
Źródło:
Econometrics. Ekonometria. Advances in Applied Data Analytics; 2019, 23, 3; 15-25
1507-3866
Pojawia się w:
Econometrics. Ekonometria. Advances in Applied Data Analytics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Poczucie śląskości wśród Ślązaków – analiza empiryczna z wykorzystaniem modeli klas ukrytych
A sense of being Silesian – an empirical analysis with the use of latent class models
Autorzy:
Genge, Ewa
Powiązania:
https://bibliotekanauki.pl/articles/425295.pdf
Data publikacji:
2013
Wydawca:
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Tematy:
latent class analysis
mixture model
model-based clustering
categorical data
Opis:
The paper focuses on latent class models and their application for quantitative data. Latent class modeling is one of multivariate analysis techniques of the contingency table and can be viewed as a special case of model-based clustering, for multivariate discrete data. It is assumed that each observation comes from one of the numbers of subpopulations, with its own probability distribution. We used latent class analysis for grouping and detecting homogeneity of Silesian people using poLCA package of R. We analyzed data collected by the Department of Social Pedagogy, University of Silesia in Katowice.
Źródło:
Econometrics. Ekonometria. Advances in Applied Data Analytics; 2013, 4(42); 48-59
1507-3866
Pojawia się w:
Econometrics. Ekonometria. Advances in Applied Data Analytics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-4 z 4

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