Abstract
This paper describes a method to generate classification rules by using an interactive multidimensional data visualization and classification tool, called PolyCluster. PolyCluster is a system that adopts state-of-the-art algorithms for data visualization and integrates human domain knowledge into the construction process of classification rules. In addition, PolyCluster proposes a pair of novel and robust measurements, called the Average External Connecting Distance and the Average Internal Connecting Distance to evaluate the quality of the induced clusters. Experimental evaluation shows that PolyCluster is a visual-based approach that offers numerous improvements over previous visual-based techniques.
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© 2004 Springer-Verlag Berlin Heidelberg
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Liu, D., Sprague, A.P. (2004). Mining Multi-dimensional Data with Visualization Techniques. In: Zhang, C., W. Guesgen, H., Yeap, WK. (eds) PRICAI 2004: Trends in Artificial Intelligence. PRICAI 2004. Lecture Notes in Computer Science(), vol 3157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28633-2_101
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DOI: https://doi.org/10.1007/978-3-540-28633-2_101
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22817-2
Online ISBN: 978-3-540-28633-2
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