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Detecting data inconsistencies by multiple target rules

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  • Kalaivany Natarajan
  • Jiuyong Li
  • Andy Koronios

Abstract

Data quality problems are common in large databases. One main data quality problem is data inconsistencies. Data mining techniques can be used to predict inconsistent values. One of the main techniques is association rule mining. Association rules identify relationships between attribute values and can be used to find out inconsistent values. In this paper, we use multiple target rules to identify inconsistent values. Multiple target rules are an extension of association rules and use a set of disjunctive attribute values as consequences. Traditional association rules predict inconsistent values by single or multiple conjunctive RHS rules. The coverage of traditional association rules is limited because of the high confidence requirement. We propose to extend RHS to multiple disjunctive rules. The coverage of multiple disjunctive rules has been extended. Prediction power of multiple disjunctive rules is higher than the traditional association rules.

Suggested Citation

  • Kalaivany Natarajan & Jiuyong Li & Andy Koronios, 2012. "Detecting data inconsistencies by multiple target rules," International Journal of Business and Systems Research, Inderscience Enterprises Ltd, vol. 6(3), pages 296-312.
    • Handle: RePEc:ids:ijbsre:v:6:y:2012:i:3:p:296-312
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    Cited by:

    1. Lin, Weiqiang, 2014. "The politics of flying: aeromobile frictions in a mobile city," Journal of Transport Geography, Elsevier, vol. 38(C), pages 92-99.

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