2015 AClusteringBasedFrameworktoCont

• (Fisher et al., 2015) ⇒ Jeffrey Fisher, Peter Christen, Qing Wang, and Erhard Rahm. (2015). “A Clustering-Based Framework to Control Block Sizes for Entity Resolution.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783396

Subject Headings: Record Linkage.

Cited By

• http://scholar.google.com/scholar?q=%222015%22+A+Clustering-Based+Framework+to+Control+Block+Sizes+for+Entity+Resolution
• http://dl.acm.org/citation.cfm?id=2783258.2783396&preflayout=flat#citedby

Quotes

Author Keywords

• Blocking; clustering; data cleaning; data mining; indexing; record linkage

Abstract

Entity resolution (ER) is a common data cleaning task that involves determining which records from one or more data sets refer to the same real-world entities. Because a pairwise comparison of all records scales quadratically with the number of records in the data sets to be matched, it is common to use blocking or indexing techniques to reduce the number of comparisons required. These techniques split the data sets into blocks and only records within blocks are compared with each other. Most existing blocking techniques do not provide control over the size of the generated blocks, despite this control being important in many practical applications of ER, such as privacy-preserving record linkage and real-time ER. We propose two novel hierarchical clustering approaches which can generate blocks within a specified size range, and we present a penalty function which allows control of the trade-off between block quality and block size in the clustering process. We evaluate our techniques on three real-world data sets and compare them against three baseline approaches. The results show our proposed techniques perform well on the measures of pairs completeness and reduction ratio compared to the baseline approaches, while also satisfying the block size restrictions.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 AClusteringBasedFrameworktoCont	Erhard Rahm Peter Christen Jeffrey Fisher Qing Wang			A Clustering-Based Framework to Control Block Sizes for Entity Resolution				10.1145/2783258.2783396		2015