COMPARATIVE STUDY OF VARIOUS METHODS OF CLASSIFICATION TECHNIQUES USING DIFFERENT DATASETS

Data mining or knowledge discovery is the process of analyzing data from different perspectives and summarizing it into useful information. Data mining is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Weka is a data mining tools. It is contain the many machine leaning algorithms. It is provide the facility to classify our data through various algorithms. In this paper we are studying the various classification algorithms. Classification is one of the important features of data mining as a technique for modeling of forecasts. In other words, classification is the process of dividing the data to some groups that can act either dependently or independently. Our main aim to show the comparison of the various classification algorithms with weka on various datasets and find out which algorithm will be most suitable for the users.