Large Data Set Mining With Naive Bayes Classifier and Association Rule

Computer Science

Nowadays, large data contains limitless business opportunities. Companies and organizations begin to analyze their stored data to predict their potential customers and business decisions using data mining tools as examples Naïve Bayes Classifier, Association Rule Mining, Decision Tree and other famous algorithms. An mining processing output produced accurate classification result it may help companies leading in its industry. Companies seek to find feasible business intelligences to obtain reliable prediction results. In this paper proposed method used an association rule mining to improve Naïve Bayes Classifier. Naïve Bayes Classifier is one of the famous algorithm in large data classification but based on an independent assumptions between features. Association rule mining is standard and useful for discovering relations between inputs in big data analysis. Proposed system use bank marketing data set to illustrate in this work. In general, this work is helpful to all the business data set.

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