Data mining with decision trees and decision rules
Future Generation Computer SystemsPublished 1 November 1997
Chidanand Apté, Sholom M. Weiss
Citations306
SJR quartileQ1
SJR score1.55
SNIP2.23
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TL;DR
This paper describes the use of decision tree and rule induction in data-mining applications and presents a synopsis of some major state-of-the-art tree andrule mining methodologies, as well as some recent advances.
Abstract
This paper describes the use of decision tree and rule induction in data-mining applications. Of methods for classification and regression that have been developed in the fields of pattern recognition, statistics, and machine learning, these are of particular interest for data mining since they utilize symbolic and interpretable representations. Symbolic solutions can provide a high degree of insight into the decision boundaries that exist in the data, and the logic underlying them. This aspect makes these predictive-mining techniques particularly attractive in commercial and industrial data-mining applications. We present here a synopsis of some major state-of-the-art tree and rule mining methodologies, as well as some recent advances.
Keywords
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