Knowledge Discovery Using Data Stream Mining: An Analytical Approach

View Sample PDF

Author(s): Prasanna Lakshmi Kompalli (Gokaraju Rangaraju Institute of Engineering and Technology, India)
Copyright: 2018
Pages: 28
Source title: Social Network Analytics for Contemporary Business Organizations
Source Author(s)/Editor(s): Himani Bansal (Jaypee Institute of Information Technology, India), Gulshan Shrivastava (National Institute of Technology Patna, India), Gia Nhu Nguyen (Duy Tan University, Vietnam)and Loredana-Mihaela Stanciu (University Timisoara, Romania)
DOI: 10.4018/978-1-5225-5097-6.ch012

Keywords: Business Information Systems / Business Science Reference / Media & Communications / Social Networking

Purchase

View Knowledge Discovery Using Data Stream Mining: An Analytical Approach on the publisher's website for pricing and purchasing information.

Abstract

In recent years, advancement in technologies has made it possible for most of the present-day organizations to store and record large streams of data. Such data sets, which continuously and rapidly grow over time, are referred to as data streams. Mining of such data streams is a unique opportunity and also a challenging task. Data stream mining is a process of gaining knowledge from continuous and rapid records of data. Due to increased streaming information, data stream mining has attracted the research community in the recent past. There is voluminous literature that has been published in this domain over the past few years. Due to this, isolating the correct study would be grueling task for researchers and practitioners. While addressing a real-world problem, it would be difficult to find relevant information as it would be hidden in data streams. This chapter tries to provide solution as it is an amalgamation of all techniques used for data stream mining.

The IRMA Community

Research IRM

Knowledge Discovery Using Data Stream Mining: An Analytical Approach

Purchase

Abstract

Related Content

IRMA Sponsors