Classification is an important data mining technique with broad applications. It classifies data of various kinds. Classification is used in every field of our life. Classification is used to classify each item in a set of data into one of predefined set of classes or groups. This paper has been carried out to make a performance evaluation of Naïve Bayes and j48 classification algorithm. Naive ...

Despite its simplicity, the naive Bayes classifier has surprised machine learning researchers by exhibiting good performance on a variety of learning problems. Encouraged by these results, researchers have looked to overcome naive Bayes’ primary weakness—attribute independence—and improve the performance of the algorithm. This paper presents a locally weighted version of naive Bayes that relaxe...

We propose an empirical Bayes method for variable selection and coefficient estimation in linear regression models. The method is based on a particular hierarchical Bayes formulation, and the empirical Bayes estimator is shown to be closely related to the LASSO estimator. Such a connection allows us to take advantage of the recently developed quick LASSO algorithm to compute the empirical Bayes...

[Abstract] This paper discussed some job scheduling algorithms for Hadoop platform, and proposed a jobs scheduling optimization algorithm based on Bayes Classification viewing the shortcoming of those algorithms which are used. The proposed algorithm can be summarized as follows. In the scheduling algorithm based on Bayes Classification, the jobs in job queue will be classified into bad job and...

In this paper, we present a new clustering algorithm, NBC, i.e., Neighborhood Based Clustering, which discovers clusters based on the neighborhood characteristics of data. The NBC algorithm has the following advantages: (1) NBC is effective in discovering clusters of arbitrary shape and different densities; (2) NBC needs fewer input parameters than the existing clustering algorithms; (3) NBC ca...