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Journal of Emerging Trends in Computing and Information Sciences >> Call for Papers Vol. 8 No. 3, March 2017

Journal of Emerging Trends in Computing and Information Sciences

Enhancement of Missing Values Prediction and Estimation Using Data Mining Algorithms

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Author Alaa. H. Al-Hamami, Sarah Al-Samadi
ISSN 2079-8407
On Pages 738-741
Volume No. 4
Issue No. 10
Issue Date November 01, 2013
Publishing Date November 01, 2013
Keywords Null value, Missing Value, Decision Tree Algorithm, Data Mining, and K-Nearest Neighbor Algorithm.


Abstract

In real life, users of most of the database systems face problems related to missing values, whether these values are unknown to the users or inapplicable. The Database is an image of the real life and life is always incomplete. For this reason many researchers tried to complete the database information through estimating the missing values by applying several algorithms to gain high estimated accuracy rates. This estimation is through predicting and replacing the missing values with approximate values obtained from the development of an algorithm that combines two data mining algorithms (Decision Tree and K-nearest neighbor) for estimations and predictions. The treatment also checks and validates the stored and estimated values through comparing them with internally set business rules. Using Oracle DBMS, a framework is designed and implemented for the estimation of Null Value utilizing the standard database “ADULT”, consisting of (32561) tuples, obtained from UCI Data Repository. The implementation of the present treatment shows treatment validity with a success rate of up to 80%.
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