Comparison of Classification Algorithms using WEKA on Various Datasets

Data mining is a step in the knowledge discovery process consisting of data mining algorithms that used to finds patterns or models in data. Data Mining also can be define as an analytic process designed to explore large amounts of data in search for consistent patterns and systematic relationships between variables and then to validate the findings by applying the detected patterns to new subsets of data. Classification is the most commonly applied data mining technique, which employs a set of pre-classified examples to develop a model that can classify the population of records at large. In classification techniques a model is built based on training data and applied to test data. WEKA is an open source data mining tool which includes implementation of data mining algorithms. Using WEKA we have compared the ADTree, Bayes Network, Decision Table, J48, Logistic, Naive Bayes, NBTree, PART, RBFNetwork and SMO algorithms. To compare these algorithms we have used five datasets.