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)


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.


Data mining Attribute Missing values Closest fit Approach 


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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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