Advertisement

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)

Abstract

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.

Keywords

Data mining Clustering Blood bank Data reduction 

Notes

Acknowledgement

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.

References

  1. 1.
    Selvamani K., Rai, A.K.: A novel technique for online blood bank management. In: International Conference on Intelligent Computing, Communication and Convergence (ICCC-2014) Conference, Inter science Institute of Management, Technology, Bhubaneswar, Odisha, India (2014)Google Scholar
  2. 2.
    Patil, R., Poi, M., Pawar, P., Patil, T., Ghuse, N.: Blood donors safety in Data Mining. In: 2015 International Conference on Green Computing and Internet of Things (2015)Google Scholar
  3. 3.
    Adsul, A.C., Bhosale, V.K.: Automated blood bank system using raspberry pi. Int. Res. J. Eng. Technol. (IRJET) 04(12) (2017). e-ISSN: 2395-0056Google Scholar
  4. 4.
    Open Government, Data. https://data.gov.in
  5. 5.
    Bala Senthil Murugan, L., Julian, A.: Design and implementation of automated blood bank using embedded systems. In: IEEE Sponsored 2nd International Conference on Innovations in Information, Embedded and Communication systems, iCIIECS (2015)Google Scholar
  6. 6.
    Adewumi, A., Budlender, N., Olusanya, M.: Optimizing the assignment of blood in a blood banking system: some initial results. In: WCCI 2012 IEEE World Congress on Computational Intelligence, 10–15 June 2012, Brisbane, Australia (2012)Google Scholar
  7. 7.
    Berkhin, P.: A survey of clustering data mining techniques. In: Kogan, J., Nicholas, C., Teboulle, M. (eds.) Grouping Multidimensional Data, pp. 25–71. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Fu, T.: A review on time series data mining. Eng. Appl. Artif. Intell. 24, 164–181 (2011).  https://doi.org/10.1016/j.engappai2010/09/07CrossRefGoogle Scholar
  9. 9.
    Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min. Knowl. Discov. 2, 283–304 (1998)CrossRefGoogle Scholar
  10. 10.
    Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31, 651–666 (2010)CrossRefGoogle Scholar
  11. 11.
    Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Addison Wesley, London (2005)Google Scholar
  12. 12.
    Sundaram, S., Santhanam, T.: A comparison of blood donor classification data mining models. J. Theor. Appl. Inform. Technol. 30(2), 31 (2011)Google Scholar
  13. 13.
    Ramachandran, P., et al.: Classifying blood donors using data mining techniques. IJCST 1(1) (2011)Google Scholar

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

Personalised recommendations