Dengue Prediction Using Hierarchical Clustering Methods

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10844)

Abstract

The occurrence of dengue is rapidly increasing in every year. Considering the welfare of the public, it is essential to have detailed study on the affected areas of dengue and its intensity for the control of disease. This paper uses hierarchical clustering technique to classify the data of dengue cases reported and deaths occurred in various states of India. An agglomerative clustering of ward method is used for clustering. The outcomes are represented in Indian map using shape file with RStudio. The data is predicted for 2018, by logarithmic transformation using linear models of regression. K-Nearest Neighbour algorithm is used for predicting the cluster data for 2018. The results have shown that the frequency of dengue happening or the intensity is considerably reduced in many states.

Keywords

Clustering Prediction Hierarchical clustering Linear model K-Nearest Neighbour (KNN) 

References

  1. 1.
    Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high dimensional data for data mining applications. vol. 27. ACM (1998)CrossRefGoogle Scholar
  2. 2.
    Chen, T.S., Tsai, T.H., Chen, Y.T., Lin, C.C., Chen, R.C., Li, S.Y., Chen, H.Y.: A combined k-means and hierarchical clustering method for improving the clustering efficiency of microarray. In: Proceedings of the 2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005, pp. 405–408. IEEE (2005)Google Scholar
  3. 3.
    Chipman, H., Tibshirani, R.: Hybrid hierarchical clustering with applications to microarray data. Biostatistics 7(2), 286–301 (2005)CrossRefGoogle Scholar
  4. 4.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226–231 (1996)Google Scholar
  5. 5.
    Guha, S., Rastogi, R., Shim, K.: CURE: an efficient clustering algorithm for large databases. In: ACM SIGMOD Record, vol. 27, pp. 73–84. ACM (1998)CrossRefGoogle Scholar
  6. 6.
    Hales, S., De Wet, N., Maindonald, J., Woodward, A.: Potential effect of population and climate changes on global distribution of dengue fever: an empirical model. Lancet 360(9336), 830–834 (2002)CrossRefGoogle Scholar
  7. 7.
    Going viral: How dengue has widened its grip across India \(|\) health \(|\) Hindustan Times. https://www.hindustantimes.com/health/going-viral-dengue-widens-grip-across-india/story-qT4y5zXLzPtcSW6xOptKGO.html
  8. 8.
    Hinneburg, A., Keim, D.A., et al.: An efficient approach to clustering in large multimedia databases with noise. In: KDD, vol., 98, pp. 58–65 (1998)Google Scholar
  9. 9.
    Isa, D., Kallimani, V., Lee, L.H.: Using the self organizing map for clustering of text documents. Expert Syst. Appl. 36(5), 9584–9591 (2009)CrossRefGoogle Scholar
  10. 10.
    Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis, vol. 344. Wiley, Hoboken (2009)MATHGoogle Scholar
  11. 11.
    Lindsay, S., Birley, M.: Climate change and malaria transmission. Ann. Trop. Med. Parasitol. 90(5), 573–588 (1996)CrossRefGoogle Scholar
  12. 12.
    Liu, Z., Sokka, T., Maas, K., Olsen, N.J., Aune, T.M.: Prediction of disease severity in patients with early rheumatoid arthritis by gene expression profiling. Hum. Genomics Proteomics: HGP, 2009 (2009)Google Scholar
  13. 13.
    Murtagh, F., Legendre, P.: Ward’s hierarchical agglomerative clustering method: which algorithms implement ward’s criterion? J. Classif. 31(3), 274–295 (2014)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Ng, R., Han, J.: Efficient and effective clustering method for spatial data mining. In: Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile, pp. 144–155 (1994)Google Scholar
  15. 15.
    Shekhar, S., Chawla, S.: Spatial Databases: A Tour, vol. 2003. Prentice Hall, Upper Saddle River (2003)Google Scholar
  16. 16.
    Silver, M., Sakata, T., Su, H.C., Herman, C., Dolins, S.B., O’Shea, M.J., et al.: Case study: how to apply data mining techniques in a healthcare data warehouse. J. Healthc. Inf. Manag. 15(2), 155–164 (2001)Google Scholar
  17. 17.
    Tapia, J.J., Morett, E., Vallejo, E.E.: A clustering genetic algorithm for genomic data mining. In: Abraham, A., Hassanien, A.E., de Carvalho, A.P.L.F. (eds.) Foundations of Computational Intelligence Volume 4, pp. 249–275. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-01088-0_11CrossRefGoogle Scholar
  18. 18.
    Tonnang, H.E., Kangalawe, R.Y., Yanda, P.Z.: Predicting and mapping malaria under climate change scenarios: the potential redistribution of malaria vectors in Africa. Malaria J. 9(1), 111 (2010)CrossRefGoogle Scholar
  19. 19.
    Wang, W., Yang, J., Muntz, R., et al.: STING: a statistical information grid approach to spatial data mining. In: VLDB. vol. 97, pp. 186–195 (1997)Google Scholar
  20. 20.
    Witthen, I., Frank, E.: Data Mining-Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers, Burlington (2000)Google Scholar
  21. 21.
    Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: an efficient data clustering method for very large databases. In: ACM SIGMOD Record, vol. 25, pp. 103–114. ACM (1996)CrossRefGoogle Scholar
  22. 22.
    Mutheneni, S.R., Morse, A.P., Caminade, C., Upadhyayula, S.M.: Dengue burden in India: recent trends and importance of climatic parameters. Emerg. Microbes Infect. 6(8), e70 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Vellore Institute of TechnologyVelloreIndia

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