@misc{Šulc_Zdeněk_Comparison_2017,
 author={Šulc, Zdeněk and Cibulková, Jana and Procházka, Jiří and Matějka, Martin},
 identifier={DOI: 10.15611/amse.2017.20.38},
 year={2017},
 rights={Wszystkie prawa zastrzeżone (Copyright)},
 publisher={Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu},
 description={20-th AMSE. Applications of Mathematics and Statistics in Economics. International Scientific Conference: Szklarska Poręba, 30 August- 3 September 2017. Conference Proceedings Full Text Papers, s. 461-472},
 language={eng},
 abstract={The economic datasets have their specifics; they usually describe human behavior or activity, which are difficult to measure. Thus, in comparison to non-economic datasets, they are less consistent. The paper analyzes differences between categorical economic and non-economic datasets in hierarchical clustering (HCA). To achieve this goal, two analyses based on 25 realworld datasets are carried out. In the first one, groups of economic and non-economic datasets are compared from the point of view of their internal characteristics based on HCA results; in the second one, homogenous groups of datasets are recognized and they are further examined by internal characteristics and graphical outputs. For each group of datasets, the most appropriate similarity measures are identified. The results show substantial differences between economic and non-economic datasets, primarily in terms of the within-cluster variability decrease. We were also successful in classification of the examined datasets into easily interpretable groups, for which suitable similarity measures were identified},
 title={Comparison of Economic and Non-economic Datasets with Categorical Variables in Hierarchical Clustering},
 type={materiały konferencyjne},
 keywords={economic datasets, categorical data, hierarchical clustering, similarity measures},
}