Enhancing THE CLUSTERING PERFORMANCE IN DATA Mining: DESIGN OF AN EFFICIENT FRAMEWORK TO ENHANCE MINING

Data mining method is generally used for determining more important information in an enormous dataset. The mining of data is a procedure of being acquainted with consistent patterns in a huge dimension of data applied to the techniques of unsupervised clustering, statistics, genetics, and radial basis function. Data mining concepts extract good information obtained from the dataset where those particular datasets are created well in clusters shape with convergence. The clustering techniques can be categorized namely as partitioning clustering, hierarchical clustering, density-based clustering, and grid-based clustering. It has more utilities to carry the data mining as an essential part of the business. We have proposed an efficient framework of clustering approach, and its method that improves clustering metrics, analyze the clustering of k-means with other approaches using the software tools, propose and analysis the fitness objective function using Genetic Algorithm (GA), and analysis the clustering metrics SSE using Radial Basis Function of Neural Network of ANN. An efficient framework is being presented for producing the good quality of clusters.