Exploiting Visualization in Knowledge Discovery

Hing-Yan Lee, Hwee-Leng Ong, Lee-Hian Quek

To date visualization has not been extensively harnessed in knowledge discovery in databases (KDD). In this paper, we show that a multi-dimensional visualization (MDV) technique can be used synergistically with a machine learning program like C4.5 to uncover new knowledge. Used together, the two approaches span the KDD spectrum between complete automation on one hand and fully manual on the other. We introduce MDV, its implementation in a tool named WinViz, and show how WinViz supports the various tasks in KDD.

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