Survey of different classification techniques for prediction in data mining

Abstract

Data mining is utilized for knowledge discovery from database for addressing issues in a particular domain. Data mining is a significant process for analyzing and obtaining required information from a large dataset. The main key objective of data mining extracts important information from the obtainable data. In general, data mining techniques includes: classification, Association, outlier detection clustering, Regression, prediction. This helps to extract the valid information from large dataset. In complexity, the large number of data creates time to time. Thus the data analysis becomes very difficult. Recently, large number of data is gathered for research purposes. Such data set includes hundreds or thousands of features. Many of the features in such data are useful information relevant to the problem. It also contains irrelevant information. Knowledge Discovery process has several steps. This paper consists of complete overview of Data preprocessing, different methods of feature selection and classification techniques used in data mining. This paper discuss about the better performance feature selection method and classification method for prediction. This paper studied Data preprocessing, different feature selection, and classification methods are used to identify the issue and improved prediction accuracy. These methods are implemented in Java language to identify accurate predictions.