The Arizona IDMatcher: A probabilistic identity matching system

G. Alan Wang, Siddharth Kaza, Shailesh Joshi, Kris Chang, Homa Atabakhsh, Hsinchun Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Various law enforcement and intelligence tasks require managing identity information in an effective and efficient way. However, the quality issues of identity information make this task non-trivial. Various heuristic based systems have been developed to tackle the identity matching problem. However, deploying such systems may require special expertise in system configuration and customization for optimal system performance. In this paper, we propose an alternative system called the Arizona IDMatcher. The system relies on a machine learning algorithm to automatically generate a decision model for identity matching. Such a system requires minimal human configuration effort. Experiments show that the Arizona IDMatcher is very efficient in detecting matching identity records. Compared to IBM Identity Resolution (a commercial, heuristic-based system), the Arizona IDMatcher achieves better recall and overall F-measures in identifying matching identities in two large-scale real-world datasets.

Original languageEnglish (US)
Title of host publicationISI 2007
Subtitle of host publication2007 IEEE Intelligence and Security Informatics
PublisherIEEE Computer Society
Number of pages7
ISBN (Print)1424413303, 9781424413300
StatePublished - 2007
EventISI 2007: 2007 IEEE Intelligence and Security Informatics - New Brunswick, NJ, United States
Duration: May 23 2007May 24 2007

Publication series

NameISI 2007: 2007 IEEE Intelligence and Security Informatics


OtherISI 2007: 2007 IEEE Intelligence and Security Informatics
Country/TerritoryUnited States
CityNew Brunswick, NJ


  • Adaptive detection
  • Fuzzy search
  • Identity matching
  • Identity resolution
  • Naïve bayes

ASJC Scopus subject areas

  • Computer Science(all)
  • Control and Systems Engineering


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