Quality and Complexity Measures for Data Linkage and Deduplication
-
Altmetric Citations
Description
Deduplicating one data set or linking several data sets are increasingly important tasks in the data preparation steps of many data mining projects. The aim of such linkages is to match all records relating to the same entity. Research interest in this area has increased in recent years, with techniques originating from statistics, machine learning, information retrieval, and database research being combined and applied to improve the linkage quality, as well as to increase performance and...[Show more]
dc.contributor.author | Christen, Peter | |
---|---|---|
dc.contributor.author | Goiser, Karl | |
dc.date.accessioned | 2015-12-08T22:33:28Z | |
dc.identifier.isbn | 9783540449119 | |
dc.identifier.uri | http://hdl.handle.net/1885/34693 | |
dc.description.abstract | Deduplicating one data set or linking several data sets are increasingly important tasks in the data preparation steps of many data mining projects. The aim of such linkages is to match all records relating to the same entity. Research interest in this area has increased in recent years, with techniques originating from statistics, machine learning, information retrieval, and database research being combined and applied to improve the linkage quality, as well as to increase performance and efficiency when linking or deduplicating very large data sets. Different measures have been used to characterise the quality and complexity of data linkage algorithms, and several new metrics have been proposed. An overview of the issues involved in measuring data linkage and deduplication quality and complexity is presented in this chapter. It is shown that measures in the space of record pair comparisons can produce deceptive quality results. Various measures are discussed and recommendations are given on how to assess data linkage and deduplication quality and complexity. | |
dc.publisher | Springer | |
dc.relation.ispartof | Quality Measures in Data Mining: Studies in Computational Intelligence | |
dc.relation.isversionof | 1st Edition | |
dc.subject | Keywords: Data integration and matching; Data mining pre-processing; Data or record linkage; Deduplication; Quality and complexity measures | |
dc.title | Quality and Complexity Measures for Data Linkage and Deduplication | |
dc.type | Book chapter | |
local.description.notes | Imported from ARIES | |
dc.date.issued | 2007 | |
local.identifier.absfor | 080109 - Pattern Recognition and Data Mining | |
local.identifier.absfor | 080201 - Analysis of Algorithms and Complexity | |
local.identifier.ariespublication | U3594520xPUB116 | |
local.type.status | Published Version | |
local.contributor.affiliation | Christen, Peter, College of Engineering and Computer Science, ANU | |
local.contributor.affiliation | Goiser, Karl, College of Engineering and Computer Science, ANU | |
local.description.embargo | 2037-12-31 | |
local.bibliographicCitation.startpage | 127 | |
local.bibliographicCitation.lastpage | 151 | |
local.identifier.doi | 10.1007/978-3-540-44918-8_6 | |
dc.date.updated | 2015-12-08T09:36:06Z | |
local.bibliographicCitation.placeofpublication | USA | |
local.identifier.scopusID | 2-s2.0-33846428121 | |
Collections | ANU Research Publications |
Download
File | Description | Size | Format | Image |
---|---|---|---|---|
01_Christen_Quality_and_Complexity_2007.pdf | 263.69 kB | Adobe PDF | Request a copy |
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.
Updated: 17 November 2022/ Responsible Officer: University Librarian/ Page Contact: Library Systems & Web Coordinator