Please use this identifier to cite or link to this item: http://arl.liuc.it/dspace/handle/2468/4071
Title: Use of relevant principal components to define a simplified multivarate test procedure of optimal clustering
Authors: Boari, Giuseppe
Nai Ruscone, Marta
Issue Date: 2013
Publisher: Cleup
Bibliographic citation: Boari Giuseppe, Nai Ruscone Marta (2013), Use of relevant principal components to define a simplified multivarate test procedure of optimal clustering. In: Minerva Tommaso, Morlini Isabella, Palumbo Francesco, eds. (2013), Cladag 2013: 9th meeting of the classification and data analysis group.
Abstract: Clustering is the problem of partitioning data into a finite number, k, of homogeneous and separate groups, called clusters. A good choice of k is essential for obtaining meaningful clusters. The intraclass correlation coefficient r is frequently used to measure the degree of intragroup resemblance (for example of characteristics such as blood pressure, weight and height). The theory concerning r is well established for single variables analysis (Sheff`e, 1959; Rao, 1973). In this paper, this task is addressed by means of a multiple test procedure defining the optimal cluster solution under normality assumption of the involved variables. Relevant principal components are used to define a simplified multivariate test of null intraclass correlation procedure and the proposal of a new statistical stopping rule is evaluated.
URI: http://arl.liuc.it/dspace/handle/2468/4071
Journal/Book: Cladag 2013: 9th meeting of the classification and data analysis group
ISBN: 978-88-6787-117-9
Appears in Collections:Contributo in atti di convegno

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