Metrics for the support of visual exploration of components in data mining models
Keywords:
Data mining, Visual data mining, Visualization of data mining models, Metrics for clustersAbstract
The exploration of Data Mining (DM) models using visual representation techniques and integrated interaction mechanisms offers advantages for analysts in understanding data models. This paper describes the definition, adaptation, and implementation of a set of metrics to validate and complement the visual analysis of a DM model using distance and similarity metrics applied to model components. A case study is presented using a DM model generated by the Decision Tree (DT) technique combined with Kohonen maps or Self-Organizing Map (SOM), confirming the validity of the proposed metrics.
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