Hesitant k-Nearest Neighbor (HK-nn) Classifier for Document Classification and Numerical Result Analysis

  • Neeraj Sahu
  • R. S. Thakur
  • G. S. Thakur
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 236)


This paper presents new approach Hesitant k-nearest neighbor (HK-nn)-based document classification and numerical results analysis. The proposed classification HK-nn approach is based on hesitant distance. In this paper, we have used hesitant distance calculations for document classification results. The following steps are used for classification: data collection, data pre-processing, data selection, presentation, analysis, classification process and results. The experimental results are evaluated using MATLAB 7.14. The Experimental results show proposed approach that is efficient and accurate compared to other classification approach.


Hesitant k-nearest neighbor Hesitant distance Classification Data mining 



This work is supported by research grant from MPCST, Bhopal M.P., India, Endt.No. 2427/CST/R&D/2011 dated 22/09/2011.


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Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Singhania University RajasthanJhunjhunuIndia
  2. 2.MANITBhopalIndia

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