Random forests are invariant and robust estimators that can fit complex interactions between input data of different types and binary, categorical, or continuous outcome variables, including those with multiple dimensions. In addition to these desirable properties, random forests impose a...

The goal of the paper is to present the framework for combining clustering and classification for churn management in telecommunications. Considering the value of market segmentation, we propose a three-stage approach to explain and predict the churn in telecommunications separately for...

Decision tree modelling, as one of data mining techniques, is used for credit scoring of bank customers. The main problem is the construction of decision trees that could classify customers optimally. This study presents a new hybrid mining approach in the design of an effective and appropriate...

Binary decision trees are not adequate to make the decisions more factual and lucid; rather we have to renovate this technique so that it can congregate the real world postulation and be able to support the decision making even in case of extraneous and mislaid data. This paper develops a...