Ensemble Framework for Big Data Stream Mining

Abstract

The rapid development of industry enterprises, the large amount of data generated by these originalities and the exponential growth of industrial business website are the causes that lead to different types of big data and data stream problem. There are many stream data mining algorithms for classification and clustering with their specific properties and significance key features. Ensemble classifiers help to improve the best predictive performance results among these up-to-date algorithms. In ensemble methods, different kinds of classifiers and clusters are trained rather than training single classifier. Their prediction machine learning results are combined to a voting schedule. This paper presented a framework for stream data mining by taking the benefits of assembling technology based on miss classification stream data. Experiments are carried out with real world data streams. The experimental performance results are compared with the modern popular ensemble techniques such as Boosting and Bagging. The increasing in accuracy rate and the reducing in classification time can be seen from the test results.