@article{Pacáková:157585,
      recid = {157585},
      author = {Pacáková, Z. and Poláčková, J.},
      title = {Hierarchical Cluster Analysis – Various Approaches to Data  Preparation},
      journal = {AGRIS on-line Papers in Economics and Informatics},
      address = {2013-09-30},
      number = {665-2016-44955},
      series = {5},
      pages = {11},
      month = {Sep},
      year = {2013},
      abstract = {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 clusters which was about ten  percent higher for the principal component scores (57.8%  compared to 47%). Therefore it can be stated, that in case  of using principal component scores as an input variables  for cluster analysis with explained proportion high enough  (about 92% for in our analysis), the loss of information is  lower compared to data reduction on the basis of  correlation analysis.},
      url = {http://ageconsearch.umn.edu/record/157585},
      doi = {https://doi.org/10.22004/ag.econ.157585},
}