Department of Software Technology
Vienna University of Technology
Cluster Connections: A visualization technique to reveal cluster boundaries in self-organizing maps
The self-organizing map is one of the most prominent unsupervised learning
architectures used to visualize the similarities of high-dimensional
input structures.
What remains by no means straight-forward, is an explicit representation
of cluster boundaries in the final two-dimensional map display.
The detection of these boundaries rather requires some amount of insight
into the inherent structure of the input data which may not be expected
in real-world application scenarios.
In this paper we address this deficiency by suggesting an extension to the
standard map representation that leads to an easy recognition of
cluster boundaries.
The general idea
%of the proposed extension
is the visualization of clusters
within the input data items by connecting units representing similar
data items while disconnecting units representing dissimilar data items.
As a result we get a grid of connected nodes where the intensity
of the connection mirrors the similarity of the underlying data items.
Such a representation allows intuitive analysis of the similarities inherent
in the input data without the necessity of substantial prior knowledge, and
an intuitive recognition of cluster boundaries.
Keywords Exploratory Data Analysis, Self-Organizing Map, Visual Representation, Cluster Detection
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Comments: rauber@ifs.tuwien.ac.at