Performance analysis of classification techniques using different parameters

Data mining technique is the process of analyzing data from different perspectives and summarizing it into useful information. Classification refers to the data mining problem of attempting to predict the category of categorical data by building a model based on some predictor variables. The goal of data classification is to organize and categorize data in distinct classes. It does not require any priori knowledge of the class statistical distribution in data sources. ANN can be trained to distinguish the criteria used to classify and it can do so in a generalized manner allowing successful classification of new inputs not used during training. Back propagation as a training algorithm for ANN works well for classification. This Paper shows the issue of improving the fitness of BPN algorithm and the performance analysis of various classification techniques like Naïve Bayes, Bayesian network, Support Vector Machine and GABPN discussed.