In this paper a robust fuzzy k-means clustering model for interval valued data is introduced. The peculiarity of the proposed model is the capability to manage anomalous interval valued data by reducing the effects of such outliers in the clustering model. In the interval case, the concept of anomalous data involves both the center and the width (the radius) of an interval. In order to show how our model works the results of a simulation experiment and an application to real interval valued data are discussed. © Springer-Verlag 2006.
A robust fuzzy k-means clustering model for interval valued data / D'Urso, Pierpaolo; Giordani, Paolo. - In: COMPUTATIONAL STATISTICS. - ISSN 0943-4062. - 21:2(2006), pp. 251-269. [10.1007/s00180-006-0262-y]
A robust fuzzy k-means clustering model for interval valued data
D'URSO, Pierpaolo;GIORDANI, Paolo
2006
Abstract
In this paper a robust fuzzy k-means clustering model for interval valued data is introduced. The peculiarity of the proposed model is the capability to manage anomalous interval valued data by reducing the effects of such outliers in the clustering model. In the interval case, the concept of anomalous data involves both the center and the width (the radius) of an interval. In order to show how our model works the results of a simulation experiment and an application to real interval valued data are discussed. © Springer-Verlag 2006.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.