A Framework to improve the Data Stream Mining using Internal Validation, External Validation, Performance Evaluation Shared Density Graph and Inlier, Outlier Detection

Abstract

Data stream mining is becoming more hectic in recent days. This paper includes the combination of many ideas to improve the data stream mining algorithm and given as a frame work. The entire framework depicts various aspects in comparing data stream mining which includes internal validation and Reclustering, external validation measure, Average case, best case, worst case of data stream mining algorithm, compute to micro, macro clustering, comparison of shared density graph of data stream mining algorithm and finally inliers, outlier detection, compare of all data stream mining algorithm for inliers and outlier detections. Each portion of the framework has been successfully proposed and implemented. The various data stream mining algorithm have been introduced in each portion of the implementation. This research work compared various data stream mining algorithm, performance of evaluation of shared density graph and micro, macro clusters. We are Publisher Four Research paper and Two conferences using the Designed Architecture.