Exploring Students Eating Habits Through Individual Profiling and Clustering Analysis
Individual well-being strongly depends on food habits, therefore it is important to educate the general population, and especially young people, to the importance of a healthy and balanced diet. To this end, understanding the real eating habits of people becomes fundamental for a better and more effective intervention to improve the students’ diet. In this paper we present two exploratory analyses based on centroid-based clustering that have the goal of understanding the food habits of university students. The first clustering analysis simply exploits the information about the students’ food consumption of specific food categories, while the second exploratory analysis includes the temporal dimension in order to capture the information about when the students consume specific foods. The second approach enables the study of the impact of the time of consumption on the choice of the food.
KeywordsFood analytics Individual models Clustering analysis
This work is part of the project Rasupea-Mensana funded by Regione Toscana on PRAF 2012–2015 funds as part of the “Nutrafood” project. Rasupea-Mensana is promoted by the University of Pisa and the Scuola Superiore Sant’Anna in collaboration with the Regional Agency for the Right to University Study of Tuscany (Azienda Regionale per il Diritto allo Studio Universitario Toscana - DSU) and Pharmanutra. This work is also partially supported by the EU H2020 Program under the funding scheme “INFRAIA-1-2014-2015: Research Infrastructures”, grant agreement 654024 “SoBigData” (http://www.sobigdata.eu). The authors thank the staff of DSU (as part of Rasupea) and of University of Pisa for providing data and support for data linkage.
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