Clustering-Based Decision Tree Classifier Construction
Clustering-Based Decision Tree Classifier Construction

Summary/Abstract: This article studies data structure investigation possibilities using cluster analysis. Density structures within classes are explored to implement class decomposition in order to enhance performance of decision tree classifiers. Classes are decomposed using cluster analysis and cluster merge evaluation using decision tree classifiers. Then impact of class decomposition is shown on C4.5 and CART classifiers. The main focus is on experiments carried out with real-valued data sets. The experiments are described in a step-by-step manner to illustrate the patterns discovered which affect previously proposed patterns in class decomposition methodology.

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