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.

  1. Alireza Farhangfara, L. K. (2008). Impact of imputation of missing values on classification error for discrete data. Pattern Recognition , 3692-3705.
  2. Esther-Lydia Silva-Ramírez, R. P.-M.-C.-D.-d.-l.-V. (2011). Missing value imputation on missing completely at random data using multilayer perceptrons. Neural Networks , 121–129.
  3. Geeta Chhabra, V. V. (2017). A Comparison of Multiple Imputation Methods for data with Missing Values. Indain Journal of Science and Technology , 1-7.
  4. Ibrahim Berkan Aydilek, A. A. (2013). A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm. Information Sciences , 25–35.
  5. Irene Erlyn Wina Rachmawan, A. R. (2015). Optimization of Missing Value Imputation using Reinforcement Programming . International Electronics Symposium (IES), (pp. 128-133).
  6. Irfan Pratama, A. E. (2016). A Review of Missing Values Handling Methods on Time-Series Data. International Conference on Information Technology Systems and Innovation (ICITSI) (p. 6). Bandung-Bali : IEEE.
  7. Jason Van Hulse, T. M. (2008). A comprehensive empirical evaluation of missing value imputation in noisy software measurement data. Journal of System and Software , 691-708.
  8. Julián Luengo, S. G. (2012). On the choice of the best imputation methods for missingvalues considering three groups of classification methods. , Knowledge Information System , 77–108.
  9. Sasi, T. A. (2016). Intelligent Imputation Technique for Missing Values . International Conference on Advances in Computing, Communications and Informatics (ICACCI), (pp. 2441-2445). Jaipur, India.
  10. Schmitt P, M. J. (2015). A Comparison of Six Methods for Missing Data Imputation. Journal of Biometrics and Biostatistics , 2-6.
  11. Shichao Zhang, Z. J. (2011). Missing data imputation by utilizing information within incomplete instances. The Journal of Systems and Software , 452–459.

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.
Journal URL : http://ijsrcseit.com/CSEIT174405

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