Regional Blood Bank Count Analysis Using Unsupervised Learning Techniques

  • R. KanagarajEmail author
  • N. Rajkumar
  • K. Srinivasan
  • R. Anuradha
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 35)


Data mining methods allows finding out blood bank region based consumption model that a city poses and used to pull out the information concerning to blood bank count in regard to the number of cities in each region. K- Means clustering procedure is used for identifying the regions that has low, middle and high Blood bank counts. The data set used is available in Indian government website. To validate the proposed work, the implementation is done in both R and Weka Tool and cluster mean difference is measured.


Data mining Clustering Blood bank Data reduction 



The authors like to thank the all the anonymous reviewers for their valuable suggestions and Sri Ramakrishna Engineering College for offering resources for the implementation.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • R. Kanagaraj
    • 1
    Email author
  • N. Rajkumar
    • 2
  • K. Srinivasan
    • 1
  • R. Anuradha
    • 1
  1. 1.Sri Ramakrishna Engineering CollegeCoimbatoreIndia
  2. 2.Nehru Institute of TechnologyCoimbatoreIndia

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