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Profiling visitors of a national park in Italy through unsupervised classification of mixed data

Giulia Caruso
Gabriele d’Annunzio University, Italy - ORCID: 0000-0003-0236-6201

Adelia Evangelista
Gabriele d’Annunzio University, Italy

Stefano Antonio Gattone
Gabriele d’Annunzio University, Italy - ORCID: 0000-0002-6143-9012


ABOUT THIS CHAPTER

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.
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Keywords: Cluster analysis, mixed data, unsupervised learning, customers profiling

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Pages: 135-140

Published by: Firenze University Press

Publication year: 2021

DOI: 10.36253/978-88-5518-304-8.27

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Publication year: 2021

DOI: 10.36253/978-88-5518-304-8.27

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© 2021 Author(s)
Content licence CC BY 4.0
Metadata licence CC0 1.0

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Cita come:
Caruso, G.; Evangelista, A.; Gattone, S.; 2021; Profiling visitors of a national park in Italy through unsupervised classification of mixed data. Firenze, Firenze University Press.


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