Detecting mis-entered values in large data sets

  • Kalaivany Natarajan
  • Jiuyong Li
  • Andy Koronios
Conference paper


Data is the valuable asset of business organizations and companies. Quality data is essential for business intelligence and intelligence decision-making. Data quality is a main issue in quality information management. Data quality control has been aware of by most large business organizations. Various mechanisms have been employed to ensure obtaining quality data, for example, using electronic forms for data collection. With the popularity of collecting data from electronic forms, mis-entered values become a major source of dirty values in a database. Mis-entered values can be caused b y randomly ticking multiple choices from drop down selection lists. These dirty values are more inconspicuous than traditional data entry errors and misspellings since mis-entered values have right spelling and normally do not caused integrity violation. In this paper, we discuss some data mining methods that are used for detecting mis-entered values in large data sets. We present a framework for detecting mis-entered values using association rules.


Cervical Cancer Association Rule Data Cleaning Data Mining Method Unbiased Sample 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Kalaivany Natarajan
    • 1
    • 2
  • Jiuyong Li
    • 1
    • 2
  • Andy Koronios
    • 1
    • 2
  1. 1.CRC for Integrated Engineering Asset ManagementBrisbaneAustralia
  2. 2.System Integration and IT School of Computer and Information ScienceUniversity of South AustraliaMawson LakesAustralia

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