- ASA 2021 Statistics and Information Systems for Policy Evaluation
- A cura di 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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Informazioni sul capitolo
Titolo del capitolo
Profiling visitors of a national park in Italy through unsupervised classification of mixed data
Autori
Giulia Caruso, Adelia Evangelista, Stefano Antonio Gattone
Lingua
English
DOI
10.36253/978-88-5518-304-8.27
Opera sottoposta a peer review
Anno di pubblicazione
2021
Copyright
© 2021 Author(s)
Licenza d'uso
Licenza dei metadati
Informazioni bibliografiche
Titolo del libro
ASA 2021 Statistics and Information Systems for Policy Evaluation
Sottotitolo del libro
Book of short papers of the opening conference
Curatori
Bruno Bertaccini, Luigi Fabbris, Alessandra Petrucci
Opera sottoposta a peer review
Anno di pubblicazione
2021
Copyright
© 2021 Author(s)
Licenza d'uso
Licenza dei metadati
Editore
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
Collana
Proceedings e report
ISSN della collana
2704-601X
e-ISSN della collana
2704-5846