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Tumminello, M., Micciché, S., Dominguez, L.J., Lamura, G., Melchiorre, M.G., Barbagallo, M. and Mantegna, R.N. (2011) Happy Aged People Are All Alike, While Every Unhappy Aged Person Is Unhappy in Its Own. PLoS ONE, 6, e23377.
http://dx.doi.org/10.1371/journal.pone.0023377

has been cited by the following article:

• TITLE: A New Approach to Investigate Students’ Behavior by Using Cluster Analysis as an Unsupervised Methodology in the Field of Education

  AUTHORS: Onofrio Rosario Battaglia, Benedetto Di Paola, Claudio Fazio

  KEYWORDS: Education, Unsupervised Methods, Hierarchical Clustering, Not-Hierarchical Clustering, Quantitative Analysis

  JOURNAL NAME: Applied Mathematics, Vol.7 No.15, September 12, 2016

  ABSTRACT: The problem of taking a set of data and separating it into subgroups where the elements of each subgroup are more similar to each other than they are to elements not in the subgroup has been extensively studied through the statistical method of cluster analysis. In this paper we want to discuss the application of this method to the field of education: particularly, we want to present the use of cluster analysis to separate students into groups that can be recognized and characterized by common traits in their answers to a questionnaire, without any prior knowledge of what form those groups would take (unsupervised classification). We start from a detailed study of the data processing needed by cluster analysis. Then two methods commonly used in cluster analysis are before described only from a theoretical point a view and after in the Section 4 through an example of application to data coming from an open-ended questionnaire administered to a sample of university students. In particular we describe and criticize the variables and parameters used to show the results of the cluster analysis methods.