Clustering-Based Decision Tree Classifier Construction
Technological and Economic Development of Economy 2010
Inese Poļaka, Arkādijs Borisovs

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.


Atslēgas vārdi
classification, class decomposition, cluster analysis, decision trees, data mining
DOI
10.3846/tede.2010.47

Poļaka, I., Borisovs, A. Clustering-Based Decision Tree Classifier Construction. Technological and Economic Development of Economy, 2010, Vol.16, Iss.4, 765.-781.lpp. Pieejams: doi:10.3846/tede.2010.47

Publikācijas valoda
English (en)
RTU Zinātniskā bibliotēka.
E-pasts: uzzinas@rtu.lv; Tālr: +371 28399196