An Effective Data stream mining Algorithm for Inliers and Outlier Detection in Micro, macro clusters

  • L.Ramesh, S.Gopinathan

Abstract

In the fast growing world applications are generating data in enormous volumes called data streams. Data stream is imaginably large, continual, rapid flow of information and in data mining the important tool is called clustering, hence data stream clustering (DSC) can be said as active research area. Recent attention of data stream clustering is through the applications that contain large amounts of streaming data. Data stream clustering is used in many areas such as weather forecasting, financial transactions, website analysis, sensor network monitoring, e-business, telephone records and telecommunications. In this, paper proposes data stream mining Algorithm improvement which deals with inlier and outlier detection using the proposed approach. The performed experiments show the effectiveness of data stream mining Algorithm in detecting and dealing with inlier and outliers from the stream, when compared  potentially of the proposed approach to be used in other micro cluster based data stream algorithms.

Published
2021-01-01
Section
Articles