Optimized data analysis through hybrid approach over trusted security environment

Abstract

For those who can uncover the knowledge hidden inside this data, this offers enormous opportunity, but it also creates new difficulties. In this study, we explore how the contemporary discipline of data mining might be used to glean usable information from the data that surrounds us. k Machine learning techniques include genetic algorithms, Bayesian approaches, and nearest neighbor. By combining these approaches and algorithms, a hybrid method is created in this study. The goal is to successfully categorize data by removing any information that makes it harder to learn. According to solid facts at hand, a novel data set formation strategy is suggested. Five datasets for machine learning from UCI are used in the testing procedure. These data sets pertain to the iris, breast cancer, glass, yeast, and wine. The success of the research is taken into consideration when test findings are analyzed in conjunction with prior efforts.