Title:
Validating and Refining Clusters via Visual Rendering
Validating and Refining Clusters via Visual Rendering
Author(s)
Chen, Keke
Liu, Ling
Liu, Ling
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Abstract
Clustering is an important technique for understanding of
large multi-dimensional datasets. Most of clustering
research to date has been focused on developing automatic
clustering algorithms and cluster validation methods. The
automatic algorithms are known to work well in dealing
with clusters of regular shapes, e.g. compact spherical
shapes, but may incur higher error rates when dealing with
arbitrarily shaped clusters. Although some efforts have
been devoted to addressing the problem of skewed datasets,
the problem of handling clusters with irregular shapes is
still in its infancy, especially in terms of dimensionality of
the datasets and the precision of the clustering results
considered. Not surprisingly, the statistical indices works
ineffective in validating clusters of irregular shapes, too. In
this paper, we address the problem of clustering and
validating arbitrarily shaped clusters with a visual
framework (VISTA). The main idea of the VISTA
approach is to capitalize on the power of visualization and
interactive feedbacks to encourage domain experts to
participate in the clustering revision and clustering
validation process. The VISTA system has two unique
features. First, it implements a linear and reliable
visualization model to interactively visualize multidimensional
datasets in a 2D star-coordinate space. Second,
it provides a rich set of user-friendly interactive rendering
operations, allowing users to validate and refine the cluster
structure based on their visual experience as well as their
domain knowledge.
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Date Issued
2003
Extent
611472 bytes
Resource Type
Text
Resource Subtype
Technical Report