PolyCluster: an interactive visualization approach to construct classification rules

This paper introduces a system, called PolyCluster, which adopts state-of-the-art algorithms for data visualization and integrates human domain knowledge into the construction process of classification rules. By utilizing PolyCluster, users can obtain the visual representation for underlying dataset...

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Bibliographic Details
Main Authors: Danyu Liu, Sprague, A.P., Gray, J.G.
Format: Conference Proceeding
Language:English
Subjects:
Classification tree analysis
Clustering algorithms
Concurrent computing
Data mining
Data visualization
Decision trees
Feedback
Humans
Information science
Multidimensional systems
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Summary:This paper introduces a system, called PolyCluster, which adopts state-of-the-art algorithms for data visualization and integrates human domain knowledge into the construction process of classification rules. By utilizing PolyCluster, users can obtain the visual representation for underlying datasets, and utilize that information to draw polygons to encompass wellformed clusters. Each polygon, along with its corresponding projection plane and associated attributes (or dimensions), will be saved as a classification rule, called a PolyRule, for later prediction tasks. Experimental evaluation shows that PolyCluster is a visual-based approach that offers numerous improvements over previous visual-based techniques. It also can help users to obtain additional knowledge from current datasets.
DOI:10.1109/ICMLA.2004.1383525