A New Method for Estimation of Missing Data Based on Sampling Methods for Data Mining

  • Rima Houari
  • Ahcéne Bounceur
  • Tahar Kechadi
  • Tari Abdelkamel
  • Reinhardt Euler
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 225)


Today we collect large amounts of data and we receive more than we can handle, the accumulated data are often raw and far from being of good quality they contain Missing Values and noise.

The presence of Missing Values in data are major disadvantages for most Datamining algorithms. Intuitively, the pertinent information is embedded in many attributes and its extraction is only possible if the original data are cleaned and pre-treated.

In this paper we propose a new technique for preprocessing data that aims to estimate Missing Values, in order to obtain representative Samples of good qualities, and also to assure that the information extracted is more safe and reliable.


Datamining Copulas Missing Value Multidimensional Sampling Sampling 


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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Rima Houari
    • 1
  • Ahcéne Bounceur
    • 2
  • Tahar Kechadi
    • 3
  • Tari Abdelkamel
    • 1
  • Reinhardt Euler
    • 2
  1. 1.University of Abderrahmane Mira BejaiaBejaiaAlgeria
  2. 2.Lab-STICC LaboratoryEuropean University of Britanny - University of BrestBrestFrance
  3. 3.University College DublinDublinIreland

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