A Dynamic Clustering Method for Mixed Feature-Type Symbolic Data.

Authors :: Bock, H. -H.
Gaul, W.
Vichi, M.
Arabie, Ph.
Baier, D.
Critchley, F.
Decker, R.
Diday, E.
Greenacre, M.
Lauro, C.
Meulman, J.
Monari, P.
Nishisato, S.
Ohsumi, N.
Opitz, O.
Ritter, G.
Schader, M.
Weihs, C.
Batagelj, Vladimir
Bock, Hans-Hermann
Source :: Data Science & Classification; 2006, p203-210, 8p
Publication Year :: 2006
Abstract: A dynamic clustering method for mixed feature-type symbolic data is presented. The proposed method needs a previous pre-processing step to transform Boolean symbolic data into modal symbolic data. The presented dynamic clustering method has then as input a set of vectors of modal symbolic data and furnishes a partition and a prototype to each class by optimizing an adequacy criterion based on a suitable squared Euclidean distance. To show the usefulness of this method, examples with symbolic data sets are considered. [ABSTRACT FROM AUTHOR]