Review on Missing Value Imputation Techniques in Data Mining

Authors

  • 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

Keywords:

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

Abstract

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.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-09-30

Issue

Volume 2, Issue 7, September-2017

Section

Research Articles

License

Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Arjun Puri, Dr. Manoj Gupta, " Review on Missing Value Imputation Techniques in Data Mining, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 7, pp.35-40, September-2017.