Kohonen self-organizing maps for symbolic objects

• Authors: Dudek, Andrzej

Title variants

EN

Samoorganizujące się mapy Kohonena dla obiektów symbolicznych

Abstracts

EN

Article present algorithm of creating Kohonen self-organizing maps for symbolic objects along with some examples on datasets taken from symbolic data repository (http://www.ceremade.dauphine.fr/~touati/sodas-pagegarde.htm).

EN

Visualizing data in the form of illustrative diagrams and searching, in these diagrams, for structures, clusters, trends, dependencies etc. is one of the main aims of multivariate statistical analysis. In the case of symbolic data (e.g. data in form of: single quantitative value, categorical values, intervals, multi-valued variables, multi-valued variables with weights), some well-known methods are provided by suitable 'symbolic' adaptations of classical methods such as principal component analysis or factor analysis. An alternative visualization of symbolic data is obtained by constructing a Kohonen map. Instead of displaying the individual items k = 1,..., n by n points or rectangles in a two dimensional space, the n items are first clustered into a number m of mini-clusters and then these mini-clusters are assigned to the vertices of a rectangular lattice of points in the plane such that 'similar' clusters are represented by neighbouring vertices in the lattice.

Keywords

EN

Classification; visualization; symbolic data; neural networks

• Publisher: Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
• Journal: Acta Universitatis Lodziensis. Folia Oeconomica
• Year: 2008
• Volume: 216

Dates

published

2008

Contributors

author

Dudek, Andrzej

other

Chair of Econometrics and Informatics, University of Economics, Wrocław

Identifiers

URI

http://hdl.handle.net/11089/16184