An Enhanced Common Data Cleaning Framework for Data mining

Authors(2) :-Agusthiyar. R, Dr. K. Narashiman

In this information era, there is a huge availability of data but the information is not enough to meet the requirements. This creates an urgent need for data cleaning and data cleaning solutions become highly important for data mining users. Normally, data cleaning deals with detecting, eliminating errors and inconsistencies in large data sets. For any real world data set, doing this task manually is very cumbersome as it involves huge amount of human resource and time. This means several organizations spend millions of dollars per year to detect data errors. Due to this wide range of possible data inconsistencies and the sheer data volume, data cleaning is considered to be one of the biggest problems in data warehousing. Normally the data cleaning is required when multiple data sources need to be integrated. In this research work an Enhanced Common Data Cleaning (ECDC) framework has been developed and proposed.

Authors and Affiliations

Agusthiyar. R
Research Scholar, Anna University, Chennai, Tamil Nadu India
Dr. K. Narashiman
Professor & Director, AUTVS Center for Quality Management, Anna University, Chennai, Tamil Nadu India

Data cleaning, Data mining, Extract, Transform and Load (ETL), Extensible Markup Language (XML), Enhanced Common Data Cleaning (ECDC)

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Publication Details

Published in : Volume 2 | Issue 5 | September-October 2016
Date of Publication : 2016-10-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 438-444
Manuscript Number : IJSRSET1625110
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Agusthiyar. R, Dr. K. Narashiman , " An Enhanced Common Data Cleaning Framework for Data mining, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 5, pp.438-444, September-October-2016.
Journal URL : http://ijsrset.com/IJSRSET1625110

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