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Closest Fit Approach Through Linear Interpolation to Recover Missing Values in Data Mining

  • Sanjay GaurEmail author
  • Darshanaben D. Pandya
  • Deepika Soni
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1041)

Abstract

Data in the dataset is always remaining as the basic building blocks for any query and further task and decisions. If basis data is incomplete or dataset have missing values then one cannot assume about well up to date final reports. In data mining, missing values recognition and recovery is still major issue with irregular data. To overcome from such situation, there is need of statistical or numerical techniques to recover the missing values in the dataset. Missing values in the dataset or database always cause of ambiguity and its affects final results, accuracy of query and reduce decision-making capacity. The present paper is an attempt to recover missing values using closest fit approach through linear interpolation. There is application of the concept of linear approach is used to recover the missing values.

Keywords

Data mining Attribute Missing values Closest fit Approach 

References

  1. 1.
    P.D. Allison, Estimation of Linear Models with Incomplete data, Social Methodology (Jossey Bass, San Francisco, 1987), pp. 71–103CrossRefGoogle Scholar
  2. 2.
    P.D. Allison, Missing data (Sage Publication, Thousand Oaks CA, 2001)zbMATHGoogle Scholar
  3. 3.
    S.F. Buck, A method of estimation of missing values in multivariate data suitable for use with an electronic computer. J. Royal Statistical Society, Series B 2, 302–306 (1960)MathSciNetzbMATHGoogle Scholar
  4. 4.
    L. Chen, M.T. Drane, R.F. Valois, J.W. Drane, Multiple imputation for missing ordinal data. J. Mod. Appl. Stat. Methods 4(1), 288–299 (2005)CrossRefGoogle Scholar
  5. 5.
    S. Gaur, M.S. Dulawat, A perception of statistical inference in data mining. Int. J. Comput. Sci. Commun. 1(2), 653–658 (2010)Google Scholar
  6. 6.
    S. Gaur, M.S. Dulawat, Univariate analysis for data preparation in context of missing values. J. Comput. Math. Sci., 1(5), 628–635 (2010)Google Scholar
  7. 7.
    S. Gaur, M.S. Dulawat, A closest fit approach to missing attribute values in data mining. Int. J. Adv. Sci. Technol. 2(4), 18–24 (2011)Google Scholar
  8. 8.
    S. Gaur, Closest fit approach to handle odd size missing block values. Int. J. Math. Arch. 3(7) (2012)Google Scholar
  9. 9.
    J.W. Grzymala-Busse, Data with missing attribute values: generalization of in-discernibilityGoogle Scholar
  10. 10.
    Realtion and rules induction, Transactions of rough sets. Lect. Notesin Comput. Sci. J. Subline, 1, 8–95 (2004). (Springer-Verlag)Google Scholar
  11. 10.
    D.B. Rubin, Inference and missing data. Biometrika 63, 581–592 (1976)MathSciNetCrossRefGoogle Scholar
  12. 11.
    S. Sharma, S. Gaur, Contiguous agile approach to manage odd size missing block in data mining. Int. J. Adv. Res. Comput. Sci. 4(11), 214–217 (2013)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Sanjay Gaur
    • 1
    Email author
  • Darshanaben D. Pandya
    • 2
  • Deepika Soni
    • 3
  1. 1.Jaipur Engineering College and Research CenterJaipurIndia
  2. 2.Madhav UniversityPindwara, SirohiIndia
  3. 3.University of TechnologyJaipurIndia

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