An Improved SVM Classifier for Discretization of Attributes using K-Means Clustering

Abstract

Here in this broadside a novel approach for the Discretization of Nonstop Characteristics for the Classification of various datasets is proposed. The Planned Procedure implemented here works in Two Phases, in the first stage K-means Clustering is applied on the dataset to cluster the data on the basis of classes available in the dataset and second is to classify the Clustered Data using Support Vector Machine Classifier. The various Untried results achieved on different datasets proves that the planned procedure provides less mean number of cuts and reduced mean discretization time and also provides higher accuracy with better Scalability.