Regression Model Using Instance Based Learning Streams

Abstract

Data mining is concerned with the analysis of data for finding patterns and regularities in the data sets. Statistics is a mathematical science concerned with the collection, analysis, interpretation or explanation, and presentation of data. Statistics plays a very important role in the process of data mining analysis and equally visualization of data plays a very important role in decision making process. Instance Based Learning Streams is an instance-based learning algorithm used to perform regression analysis on data streams. The algorithm is able to handle large data streams with less memory and computational power. The paper aims at the implementation of Instance Based Learning Streams as an extension to the massive online analysis framework for data stream mining to develop a regression model. The study reveals that the regression analysis could be performed not only on small data sets but also on data streams as in the present case but the method of analysis will be different in the two cases. In the case of small data set the regression models are linear, multiple and polynomial, while in the case of data streams the entire analysis is performed under the massive online analysis framework by taking the two evaluation parameters basic regression performance evaluator and windows regression performance evaluator. This finding is first of its kind in literature.