SCSC2003 Abstract S91675
Beyond limits in Kohonen's self-organized clustering
Submitting Author: Prof. Miroslav Snorek
Abstract:
A lot of applications (e.g. inductive, or data driven
modeling) require the knowledge how are the input
data spread in the (usually multidimensional) input
data space. Without knowing it any inductive model
wil produce corrupted results. A lot of statistical
methods commonly referred as methods for
Exploratory Data Analysis exist. The objective of it is
to produce simplified description and summaries of
large data sets. One standard method used in EDA is
clustering, another alternative is projection of high
dimensional data sets as points on a low-dimensional,
usually 2D display. The aim of this article is the
publication of an extended method for both clustering
and projection based on Kohonen’s Self-Organizing
Maps. We use learned SOM as a model (tool) for
interactive simulation.
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