An Efficient Class-Based Data Clustering Through K-Means and KNN approach

Abstract

In this paper an efficient class-based data clustering through k-means and KNN approach were applied. It has been applied for proper data grouping and efficient classification. In this approach three dataset have been considered for the experimentation. Data preprocessing has been performed for the removal of unmatched or empty entry. Then weight assignment and normalization has been performed. K-means and k-nearest neighbor (KNN) have been applied on the dataset for the data grouping and classification purpose. Different splitting variations have been considered with higher variance. Data selection is completely random. The results obtained shows the strength of our approach though classification and class-based clustering.