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

Rima Houari 1 Ahcène Bounceur 2 Tahar Kechadi 3 Reinhardt Euler 2
2 Lab-STICC_UBO_CACS_MOCS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance, UBO - Université de Brest
Abstract : 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 the Missing Values, in order to obtain representative Samples of good quality, and also to assure that the information extracted is more safe and reliable.
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Communication dans un congrès
CCSEIT, Jun 2013, Turkey. 2013
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  • HAL Id : hal-00801464, version 1

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Rima Houari, Ahcène Bounceur, Tahar Kechadi, Reinhardt Euler. A New Method for Estimation of Missing Data Based on Sampling Methods for Data Mining. CCSEIT, Jun 2013, Turkey. 2013. 〈hal-00801464〉

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