UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 6 Issue 3
March-2019
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIRAR06056


Registration ID:
201185

Page Number

252-256

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Title

A Review on Efficient Approaches to Detect and Eliminate Data Redundancy in Large Volume of Data using Anomaly detection

Abstract

In order to finding an un-matching pattern in any dataset that does not satisfy the expected nature of the customer then Anomaly detection will these kinds of issues and Anomaly detection also finds the inconsistent data pattern, and this process is called as novelty detection, noise mining, and anomaly mining. Modern IT companies enable enterprises to detect strange events automatically in streaming data. Un-matching pattern refers error in the dataset, different pattern, duplicate data and misbehavior data. Identifying anomalies is more important in a wide range of disciplines like economic data, medical analysis, share market, insurance data and identity fraud, network malicious and programming defects. There are various types of anomalies available such as point or content anomalies, context anomalies, and collective anomalies. Some of the data are abnormal than the other entire dataset regarding meta-information is called as context anomalies. The collected data points are considered as anomalies when compared to other data in the data sheet.

Key Words

Data Mining, data preprocessing, Big data, MOMGODB, QAmodel

Cite This Article

"A Review on Efficient Approaches to Detect and Eliminate Data Redundancy in Large Volume of Data using Anomaly detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.252-256, March-2019, Available :http://www.jetir.org/papers/JETIRAR06056.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"A Review on Efficient Approaches to Detect and Eliminate Data Redundancy in Large Volume of Data using Anomaly detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp252-256, March-2019, Available at : http://www.jetir.org/papers/JETIRAR06056.pdf

Publication Details

Published Paper ID: JETIRAR06056
Registration ID: 201185
Published In: Volume 6 | Issue 3 | Year March-2019
DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.20286
Page No: 252-256
Country: -, -, - .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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