Review on Missing Value Imputation Techniques in Data Mining

Authors(2) :-Arjun Puri, Dr. Manoj Gupta

Now days, there are huge amount of data available for analysis, the main problem with the data is inconsistency. The inconsistent data (missing value) need to replace with most appropriate fit values. Some missing values are dependent on some known variable in the dataset need to be taken for further calculation. There are different methods to impute these missing values. In this paper, we discuss various technique based on their classification and also discuss their behavior in different datasets under different types of missing values.

Authors and Affiliations

Arjun Puri
Department of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
Dr. Manoj Gupta
Department of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India

Missing value imputation, data mining, data preprocessing, Techniques for missing value imputation, MCAR, MAR, NMAR.

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Publication Details

Published in : Volume 2 | Issue 7 | September 2017
Date of Publication : 2017-09-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 35-40
Manuscript Number : CSEIT174405
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Arjun Puri, Dr. Manoj Gupta, "Review on Missing Value Imputation Techniques in Data Mining", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 7, pp.35-40, September-2017.
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