Classification with K-means Clustering and Decision Tree

Classifiers can be either linear means Naive Bayes classifier or non-linear means decision trees.In this work we discusses with decision tree ,Naïve Bayes and k-means clustering .The Naive Bayes is based on conditional probabilities and affords fast, highly scalable model building and scoring. It scales linearly with the number of predictors and rows. And also build process is parallelized..Data Mining supports several algorithms that provide rules. Decision trees are among the best algorithms for data classification, providing good accuracy for many problems in relatively short time. Decision tree scoring is especially fast. The k-Means algorithm is a distance-based clustering algorithm that partitions the data into a predetermined number of clusters provided there are enough distinct cases.