Comparison of methods and analysis’ strategies for clustering the objects of the multidimensional dataset “Forest Cover Type”

Published: Jan 2, 2026
Keywords:
Multivariate data Big data Hierarchical clustering Partitional Clustering Mixed type data
Emmanouil Pratsinakis
Aristotle University of Thessaloniki
https://orcid.org/0000-0002-3725-3525
Zacharenia Kyrana
Aristotle University of Thessaloniki
https://orcid.org/0000-0001-9269-0675
Nikolaos Papafilippoy
Aristotle University of Thessaloniki
https://orcid.org/0009-0003-3148-7229
Angelos Markos
Democritus University of Thrace
George Menexes
Aristotle University of Thessaloniki
https://orcid.org/0000-0002-1034-7345
Abstract

A multidimensional and multivariate structure with mixed-type data give researchers the opportunity to use various statistical approaches and methods of data clustering (classification), through statistical packages and programming languages. The choice of clustering method used can have an impact on the results obtained, with the distance and joining method playing a key role. In this study, the Partitioning Clustering or k-means and the Hierarchical Cluster Analysis methods were compared using the "Forest Cover Type" dataset. The main objective of this study was to apply and compare these methods in the division of the data set into groups-clusters. The results of the study showed that there are several analysis strategies for clustering mixed type data, based on data coding and selection of the measurement scale of the input variables. Python exported the results faster, up to more than 100%, compared to SPSS, and it was observed that the results of both were similar except for minor differences due to numerical rounding. It was found that Hierarchical Cluster could not be performed on this or other data sets of similar size, and with the specific PC configuration used for the analyses, since both softwares "crashed". This is probable a disadvantage, as Hierarchical Cluster allows the determination of the number of clusters through dendrograms, by combining various distances and clustering joining methods, which cannot be achieved with the k-means method. Finally, it was found that the results of the Classification depend on the coding strategy and the selection of the measurement scale of the variables to be used in the analysis.

Article Details
  • Section
  • Empirical studies
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