Rule mining and classification in imperfect databases

K. K.R.G.K. Hewawasam, K. Premaratne, S. P. Subasingha, M. L. Shyu

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

18 Scopus citations


A rule-based classifier learns rules from a set of training data instances with assigned class labels and then uses those rules to assign a class label for a new incoming data instance. To accommodate data imperfections, a probabilistic relational model would represent the attributes by probabilistic functions. One extension to this model uses belief functions instead. Such an approach can represent a wider range of data imperfections. However, the task of extracting frequent patterns and rules from such a "belief theoretic" relational database has to overcome a potentially enormous computational burden. In this work, we present a data structure that is an alternate representation of a belief theoretic relational database. We then develop efficient algorithms to query for belief of itemsets, extract frequent itemsets and generate corresponding association rules from this representation. This set of rules is then used as the basis on which an unknown data instance, whose attributes are represented via belief functions, is classified. These algorithms are tested on a data set collected from a testbed that mimics an airport threat detection and classification scenario where both data attributes and threat class labels may possess imperfections.

Original languageEnglish (US)
Title of host publication2005 7th International Conference on Information Fusion, FUSION
PublisherIEEE Computer Society
Number of pages8
ISBN (Print)0780392868, 9780780392861
StatePublished - 2005
Event2005 8th International Conference on Information Fusion, FUSION - Philadelphia, PA, United States
Duration: Jul 25 2005Jul 28 2005

Publication series

Name2005 7th International Conference on Information Fusion, FUSION


Conference2005 8th International Conference on Information Fusion, FUSION
Country/TerritoryUnited States
CityPhiladelphia, PA


  • Association rules
  • Classification
  • Data ambiguities
  • Data imperfections
  • Data mining
  • Dempster-Shafer belief theory

ASJC Scopus subject areas

  • Engineering(all)


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