A Naive Bayes Algorithm of Data Mining Method for Clustering of Data
A significant research theory that will have a wide range of application in future is Data mining. The invisible information in a Data is found by using this Data mining. This paper deals with a Naive Bayes Algorithm of Data mining Method for Clustering of Data. The major part of the cluster is a feature extraction/feature selection. This implies recognition of set of options in a set, since the selection of features is considered a necessary method. In this paper, data mining algorithm of naive bayes is used to predict product recommendations. This naive bayes algorithm is implemented on a set of products with the help of WEKA data mining software tool. The handling of missing data is carried out by applying an approach of A Unique Category (AUC) for obtaining better performance than the exported classifier model. The result shows that application of this AUC handling of missing data increase the accuracy of classifier model.