Performance Evaluation of Classification Algorithm in Weka using Parallel Performance Profiling and Computing Technique

At present, the size of the data is growing rapidly. In such a situation, it is necessary to keep an eye on the speed of the data without undermining it. It is also important to note that while processing the data, its quality remains intact. This is the reason that data mining technology has become very important in the field of predictions in the scientific mining area, commercial and environment sectors. In this case, the need for parallel processing becomes important. Therefore, the aim of this paper is to analyze and perform computation times of different classification algorithms on many datasets using parallel profiling and computing techniques. Performance analysis is based on many factors, such as the unique nature of the dataset, the size, and type of the class, the diversity of the data in the data set, and so on. Many researchers are working on the optimization of classification algorithms which are not showing accurate results according to the processor (core) capacity. So, in this paper, we have displayed some simulation results which discuss the processor's size, efficiency, and workload as well as the complexity of input instructions in the group; Which will help researchers to optimize the code for maximum use of the core. At the end of the paper, we have given a comparative study of the optimized algorithm based on a parallel approach and tuned algorithm based on parallel performance.