- ASA 2021 Statistics and Information Systems for Policy Evaluation
- Edited by Bruno Bertaccini, Luigi Fabbris, Alessandra Petrucci
Profiling visitors of a national park in Italy through unsupervised classification of mixed data
- Giulia Caruso
- Adelia Evangelista
- Stefano Antonio Gattone
- © 2021 Author(s) |
- CC BY 4.0
- DOI: 10.36253/978-88-5518-304-8.27
Cluster analysis has for long been an effective tool for analysing data. Thus, several disciplines, such as marketing, psychology and computer sciences, just to mention a few, did take advantage from its contribution over time. Traditionally, this kind of algorithm concentrates only on numerical or categorical data at a time. In this work, instead, we analyse a dataset composed of mixed data, namely both numerical than categorical ones. More precisely, we focus on profiling visitors of the National Park of Majella in the Abruzzo region of Italy, which observations are characterized by variables such as gender, age, profession, expectations and satisfaction rate on park services. Applying a standard clustering procedure would be wholly inappropriate in this case. Therefore, we hereby propose an unsupervised classification of mixed data, a specific procedure capable of processing both numerical than categorical variables simultaneously, releasing truly precious information. In conclusion, our application therefore emphasizes how cluster analysis for mixed data can lead to discover particularly informative patterns, allowing to lay the groundwork for an accurate customers profiling, starting point for a detailed marketing analysis.
- Keywords:
- Cluster analysis,
- mixed data,
- unsupervised learning,
- customers profiling,
University of Chieti-Pescara G. D'Annunzio, Italy - ORCID: 0000-0003-0236-6201
University of Chieti-Pescara G. D'Annunzio, Italy - ORCID: 0000-0002-7596-9719
University of Chieti-Pescara G. D'Annunzio, Italy - ORCID: 0000-0002-6143-9012
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Chapter Information
Chapter Title
Profiling visitors of a national park in Italy through unsupervised classification of mixed data
Authors
Giulia Caruso, Adelia Evangelista, Stefano Antonio Gattone
Language
English
DOI
10.36253/978-88-5518-304-8.27
Peer Reviewed
Publication Year
2021
Copyright Information
© 2021 Author(s)
Content License
Metadata License
Bibliographic Information
Book Title
ASA 2021 Statistics and Information Systems for Policy Evaluation
Book Subtitle
Book of short papers of the opening conference
Editors
Bruno Bertaccini, Luigi Fabbris, Alessandra Petrucci
Peer Reviewed
Publication Year
2021
Copyright Information
© 2021 Author(s)
Content License
Metadata License
Publisher Name
Firenze University Press
DOI
10.36253/978-88-5518-304-8
eISBN (pdf)
978-88-5518-304-8
eISBN (xml)
978-88-5518-305-5
Series Title
Proceedings e report
Series ISSN
2704-601X
Series E-ISSN
2704-5846