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What is Data Cleaning

Encyclopedia of Information Science and Technology, Third Edition
Process of detection and correction of incomplete, corrupted or inaccurate data from a data source.
Published in Chapter:
Record Linkage in Data Warehousing
Alfredo Cuzzocrea (ICAR-CNR and University of Calabria, Italy) and Laura Puglisi (GESP Geographic Information Systems, Italy)
Copyright: © 2015 |Pages: 10
DOI: 10.4018/978-1-4666-5888-2.ch189
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More Results
Using Data Mining Techniques to Predict Obstetric Fistula in Tanzania: A Case of CCBRT
Is the process of detecting and correcting the corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty data.
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Restaurant Sales Prediction Using Machine Learning
The process of identifying and removing or correcting errors, inconsistencies, and inaccuracies in a dataset in order to improve its quality and usefulness for analysis.
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Big Data Preprocessing, Techniques, Integration, Transformation, Normalisation, Cleaning, Discretization, and Binning
Data Cleaning involves the identification and correction of errors, outliers, duplicates, or inconsistencies in raw data to improve its quality, aiming to eliminate noise and irregularities and establish a reliable foundation for subsequent analysis.
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Data Science in the Database: Using SQL for Data Preparation
Set of activities carried out to take 'dirty' data (data with problems like missing data, outliers, etc.) and transform it into 'clean' or 'tidy' data. Its focus is on solving any issues the data may have so as to get the data ready for further analysis.
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Machine Learning in Text Analysis
A sub-process in data preprocessing, where we remove punctuation, stop words, etc. from the text.
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A Machine Learning Approach to Data Cleaning in Databases and Data Warehouses
Data cleaning is the process of improving the quality of the data by modifying their form or content, for example, removing or correcting erroneous data values, filling in missing values, and so forth.
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