Analysis of Clustering and Classification Methods for Actionable Knowledge

Abstract

Data Mining becomes a vital aspect in data analysis. Study on data mining is very much depends on the performance of the clustering. Clustering before classification is termed as cluster Classifier. Recently knowledge based approached has become the key forces in data classification. Here performed a four way comparison of Logistic Regression (LR), Classification and Regression Trees (CART), Random Forest (RF) and Neural Network (NN) models using a continuous and categorical dependent variable for classification. A Customer relationship management (CRM) data set is used to run these models. Measurement of different classification accuracy methods are used to compare the performance of the models. Based on the efficient method actionable knowledge is derived from the proposed methodology. © 2017 Elsevier Ltd.