An Introduction to Duplicate Detection

An Introduction to Duplicate Detection

Feliz Nauman, Melanie Herschel
ISBN: 9781608452200 | PDF ISBN: 9781608452217
Copyright © 2010 | 87 Pages | Publication Date: 01/01/2010

BEFORE YOU ORDER: You may have Academic or Corporate access to this title. Click here to find out: 10.2200/S00262ED1V01Y201003DTM003

Ordering Options: Paperback $30.00   E-book $24.00   Paperback & E-book Combo $37.50


Why pay full price? Members receive 15% off all orders.
Learn More Here

Read Our Digital Content License Agreement (pop-up)

Purchasing Options:



With the ever increasing volume of data, data quality problems abound. Multiple, yet different representations of the same real-world objects in data, duplicates, are one of the most intriguing data quality problems. The effects of such duplicates are detrimental; for instance, bank customers can obtain duplicate identities, inventory levels are monitored incorrectly, catalogs are mailed multiple times to the same household, etc. Automatically detecting duplicates is difficult: First, duplicate representations are usually not identical but slightly differ in their values. Second, in principle all pairs of records should be compared, which is infeasible for large volumes of data. This lecture examines closely the two main components to overcome these difficulties: (i) Similarity measures are used to automatically identify duplicates when comparing two records. Well-chosen similarity measures improve the effectiveness of duplicate detection. (ii) Algorithms are developed to perform on very large volumes of data in search for duplicates. Well-designed algorithms improve the efficiency of duplicate detection. Finally, we discuss methods to evaluate the success of duplicate detection.

Table of Contents: Data Cleansing: Introduction and Motivation / Problem Definition / Similarity Functions / Duplicate Detection Algorithms / Evaluating Detection Success / Conclusion and Outlook / Bibliography

Table of Contents

Data Cleansing: Introduction and Motivation
Problem Definition
Similarity Functions
Duplicate Detection Algorithms
Evaluating Detection Success
Conclusion and Outlook
Bibliography

About the Author(s)

Feliz Nauman, Hasso Plattner Institute, Potsdam, Germany

Melanie Herschel, University of T

Related Series

Data Mining and Knowledge Discovery

Reviews

Customers who bought this product also purchased
Data Cleaning
Data Cleaning
Browse by Subject
Case Studies in Engineering
ACM Books
IOP Concise Physics
SEM Books
0 items
LATEST NEWS

Newsletter
Note: Registered customers go to: Your Account to subscribe.

E-Mail Address:

Your Name: