Manuscript Number : IJSRSET1625110
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.
Agusthiyar. R
Data cleaning, Data mining, Extract, Transform and Load (ETL), Extensible Markup Language (XML), Enhanced Common Data Cleaning (ECDC)
Yashpal Singh & Alok Singh Chauha (2005 – 2009), ‘Neural Networks In Data Mining’,Journal of Theoretical and Applied Information Technology pp. 37- 42. Publication Details
Published in :
Volume 2 | Issue 5 | September-October 2016 Article Preview
Research Scholar, Anna University, Chennai, Tamil Nadu India
Dr. K. Narashiman
Professor & Director, AUTVS Center for Quality Management, Anna University, Chennai, Tamil Nadu India
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
Journal URL :
https://ijsrset.com/IJSRSET1625110