Journal of Intelligent Information Systems

, Volume 47, Issue 1, pp 135–163 | Cite as

Evidential data mining: precise support and confidence



Associative classification has been shown to provide interesting results whenever of use to classify data. With the increasing complexity of new databases, retrieving valuable information and classifying incoming data is becoming a thriving and compelling issue. The evidential database is a new type of database that represents imprecision and uncertainty. In this respect, extracting pertinent information such as frequent patterns and association rules is of paramount importance task. In this work, we tackle the problem of pertinent information extraction from an evidential database. A new data mining approach, denoted EDMA, is introduced that extracts frequent patterns overcoming the limits of pioneering works of the literature. A new classifier based on evidential association rules is thus introduced. The obtained association rules, as well as their respective confidence values, are studied and weighted with respect to their relevance. The proposed methods are thoroughly experimented on several synthetic evidential databases and showed performance improvement.


Evidential database Evidential confidence Evidential support Associative classification 



The authors would like to express their sincere gratitude to the anonymous reviewers for their constructive and helpful comments and suggestions which have been in help to improve the quality of this paper.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Université Lille Nord de France UArtoisBéthuneFrance
  2. 2.Faculty of Sciences of TunisUniversity of Tunis ElManar, LIPAH-LRTunisTunisia

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