Data Mining is a Technique Used to Clarify and Classify Data

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Data Mining is a technique used in various domains to give meaning to the available data and different types of Data to be handled like numerical data, non-numeric data, image data...etc. In classification tree modelling the data is classified to make predictions about new data. Using old data to predict new data has the danger of being too fitted on the old data. In this we evaluated different types of data to be collected from UCI repository for classify the data using the different classification algorithms J48, Naive Bayes, Decision Tree, IBK. This paper evaluates the classification accuracy before applying the feature selection algorithms and comparing the classification accuracy after applying the feature selection with learning algorithms. 1. Introduction As computer and database technologies develop rapidly, data accumulates in a speed unmatchable by human capacity of data processing[2]. Data mining as a multidisciplinary joint effort from databases, machine learning and statistics, is championing in turning mountains of data into nuggets. Researchers and practitioners realize that in order to use data mining tools effectively, data processing is essential to successful data mining.PrimitiveThese are features which have an influence on the output and their role cannot be assumed by the rest.[1] Feature selection can be found in many areas of data mining such as classification, clustering, association rules and regression. For example, feature selection is

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