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An Analytical Framework for Data Stream Mining Methodologies Considering

Author(s):

Shabina Sayed , jodhpur national university; Shoeb Ahmed, jjtu; Dr. Rakesh Poonia, govt engg college,bikaner

Keywords:

Data Stream, Knowledge, Discovery, Concept Drift

Abstract

In today’s information society, computers are used to gather and share data anytime and anywhere. This concerns applications such as social networking, banking, telecommunication, healthcare, research, and entertainment, among others. As a result, a huge amount of data related to all human activity is gathered for storage and processing purposes. These data sets may contain interesting and useful knowledge represented by hidden patterns, but due to the volume of the gathered data, it is impossible to manually extract that knowledge. That is why data mining and knowledge discovery methods have been proposed to automatically acquire interesting, non-trivial, previously unknown and ultimately understandable patterns from very large data sets. A new class of emerging applications generates data at very high rates in the form of transient data streams. Due to their speed and size, it is impossible to store them permanently. Applications of data stream analysis can vary from critical scientific and astronomical applications to important business and financial ones. Algorithms, systems, and frameworks that address streaming challenges have been developed over the past 10 years. In this paper, we review the theoretical foundations of data stream analysis, mining data stream systems. Finally, we outline and discuss research problems in stream mining field of study.

Other Details

Paper ID: IJSRDV5I21631
Published in: Volume : 5, Issue : 2
Publication Date: 01/05/2017
Page(s): 2023-2026

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