Chapter Profiling visitors of a national park in Italy through unsupervised classification of mixed data
Caruso, Giulia
Chapter Profiling visitors of a national park in Italy through unsupervised classification of mixed data - Florence Firenze University Press 2021 - 1 electronic resource (6 p.)
Open Access
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.
Creative Commons
English
978-88-5518-304-8.27 9788855183048
10.36253/978-88-5518-304-8.27 doi
Cluster analysis mixed data unsupervised learning customers profiling
Chapter Profiling visitors of a national park in Italy through unsupervised classification of mixed data - Florence Firenze University Press 2021 - 1 electronic resource (6 p.)
Open Access
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.
Creative Commons
English
978-88-5518-304-8.27 9788855183048
10.36253/978-88-5518-304-8.27 doi
Cluster analysis mixed data unsupervised learning customers profiling