Performance Analysis of Classification Learning Methods on Large Dataset using 8 two Data Mining Tools

Data is increasing day to day thus, processing this data and selection of right method and tool is really a big problem. Computer scientists are process- ing and analysing data on different machine learning methods using various Data Mining tools to get the high accuracy of results and minimum time for building of Model. There are several data analysis and processing tools like WEKA, RapidMiner, Keel, and etc. available for the purpose of processing, analysis, modelling and etc. Still no single tool is perfect or nominated for data processing and analysis. In this concern, the authors present here a comparative and analytical research study on the performance of different classification machine learning algorithms like Naïve Bayes, KNN, IBK, Random Forest, C4.5, J48 and Data Mining tools which are WEKA and RapidMiner on a large datasets to evalu- ate their performance and analytical results with low cost of error. The data set Adult Income is taken from UCI Data repository for this research study. The significance and aim of this study is to evaluate and assess the range of performance of different machine learning methods and two diverse data mining tools on dissimilar datasets. The result of each classification method and Data mining tool is analysed and presented in the end.