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dc.contributor.authorChen, Kekeen_US
dc.contributor.authorLiu, Ling
dc.date.accessioned2005-06-17T17:38:17Z
dc.date.available2005-06-17T17:38:17Z
dc.date.issued2003en_US
dc.identifier.urihttp://hdl.handle.net/1853/6517
dc.description.abstractClustering 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.en_US
dc.format.extent611472 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.relation.ispartofseriesCC Technical Report; GIT-CC-03-57en_US
dc.subjectData clustering
dc.subjectCluster validity
dc.subjectInformation visualization
dc.subjectHuman factor in clustering
dc.titleValidating and Refining Clusters via Visual Renderingen_US
dc.typeTechnical Reporteng_US


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