| Abstract: | Exploratory graphical tools based on trimming are proposed for detecting main clusters in a given dataset. The trimming is obtained by resorting to trimmed k-means methodology. The analysis always reduces to the examination of real valued curves, even in the multivariate case. As the technique is based on a robust clustering criterium, it is able to handle the presence of different kinds of outliers. An algorithm is proposed to carry out this (computer intensive) method. As with classical k-means, the method is specially oriented to mixtures of spherical distributions. A possible generalization is outlined to overcome this drawback. |
