The Impact of Streaming Data Noise Reduction by using Chunk Based Ensemble
Vishal Jaulkar1, Nalini N2

1Nalini N, Asst. Prof. Senior, Scope Vellore Institute of Technology, University, Vellore (Tamil Nadu) India.
2Vishal Jaulkar, Scope Vellore Institute of Technology University, Vellore (Tamil Nadu) India.

Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 917-921 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6436048419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: The Propelled analytics of data streams is rapidly turning into an important territory of processing the complicated data as the quantity of requesting such applications grows continuously. Web based mining when the input data advancing over period is getting to be most important center problem. In todays real world the data is not stationary but non stationary. The properties of attributed are changing over time. As a result the data stream has concept drift and noise which affects the performance. The aim of the paper is to first present an overview of the challenges in the data streams, followed by the measures to improve the factors that affect the performance.
Keywords: Analytics, Concept Drift, Stream, Mining

Scope of the Article: Data Mining