Design and Development of Hybridized DBSCAN-NN Approach for Location Prediction to Place Water Treatment Plant

  • Mousi Perumal
  • Bhuvaneswari Velumani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 645)


Water plays an important part in all living organisms on earth by balancing the entire ecosystem; the natural resource is being exploited and contaminated due to technological growth, urbanization, and human activities. The natural resource has now changed into a precious commodity, with which many businesses flourish. The objective of the work is to identify locations to set water purification plant near water bodies such as river, pond, and lake to reuse the contaminated water for agriculture and for other basic need using Spatial Data Mining (SDM). SDM operates on location-specific analysis stored in geo-databases which are a collection of spatial and non-spatial data. The spatial and non-spatial data for Coimbatore region is collected, and the location prediction for setting water treatment plant is done through DBSCAN-NN algorithm using SDM tools.


Spatial Data Mining DBSCAN-NN Geo-databases Location prediction 


  1. 1.
    Du, Y., Liang, F., Sun, Y.: Integrating spatial relations into case-based reasoning to solve geographic problems. Known.-Based Syst. (2012)Google Scholar
  2. 2.
    Lee, A.J.T., Hong, R.W., Ko, W.M., Tsao, W.K., Lin, H.H.: Mining frequent trajectory patterns in spatial–temporal databases. J. Inf. Sci. 179, 2218–2231 (2009)CrossRefGoogle Scholar
  3. 3.
    Wang, S., Ding, G., Zhong, M.: Big Spatial Data Mining, pp. 13–21. IEEE (2013)Google Scholar
  4. 4.
    Bai, H., Ge, Y., Wang, J., Li, D., Liao, Y., Zheng, X.: A method for extracting rules from spatial data based on rough fuzzy Sets. J. Knowl. Based Syst. 57, 28–40 (2014)CrossRefGoogle Scholar
  5. 5.
    Bi, S., et al.: Spatial Data Mining in Settlement Archaeological Databases Based on Vector Features, pp. 277–281. IEEE (2008)Google Scholar
  6. 6.
    De Moraes, A.F., Bastos, L.C.: Pattern Recognition with Spatial Data Mining in Web: An Infrastructure to Engineering of the Urban Cadaster, pp. 1331–1335. IEEE (2011)Google Scholar
  7. 7.
    Brimicombe, A.J.: A dual approach to cluster discovery in point event data sets. Comput. Environ. Urban Syst. 31(1), 4–18 (2007)Google Scholar
  8. 8.
    Spielman, S.E., Thill, J.C.: Social area analysis, data mining and GIS. Comput. Environ. Urban Syst. 32(2), 110–122 (2008)Google Scholar
  9. 9.
    He, B., Fang, T., Guo, D.: Uncertainty in Spatial Data Mining, pp. 1152–1156. IEEE (2004)Google Scholar
  10. 10.
    Shekhar, S., Zhang, P., Huang, Y., Vatsavai, R.R.: Trends in spatial data mining. In: Kargupta, H., Joshi, A. (eds.) Data Mining: Next Generation Challenges and Future Directions. AAAI/MIT (2003)Google Scholar
  11. 11.
    Du, Qin, Q., Wang, Q., Ma, H.: Reasoning about topological relations between regions with broad boundaries. Int. J. Approx. Reason. 47, 219–232 (2008).  10.1016/j.ijar.2007.05.002
  12. 12.
    Yasmin, M.: Dynamic Referencing Rules Creation Using Intelligent Agent in Geo Spatial Data Mining. IEEE (2012)Google Scholar
  13. 13.
    Shengwu, H.: Method Development about Spatial Data Mining and Its Problem Analysis, vol. 2, pp. 144–147. IEEE (2011)Google Scholar
  14. 14.
    Shekhar, S., Lu, C.T., Zhang, P.: A unified approach to detection spatial outliers. GeoInformatica 7, 139–166 (2003)CrossRefGoogle Scholar
  15. 15.
    Peng, S., Fang, J., Han, C., Cheng, Z.: VegaMinerPOI: A Spatial Data Mining System for POI Datasets, pp. 1–4Google Scholar
  16. 16.
    Lee, A.J.T., Hong, R.W., Ko, W.M., Tsao, W.K., Lin, H.H.: Mining spatial association rules in image databases. Inf. Sci. 177, 1593–1608 (2007)Google Scholar
  17. 17.
    Xiao ping, L., Zheng yuan, M., Jian hua, L.: A spatial clustering method by means of field model to organize data. In: Second WRI Global Congress on Intelligent Systems (GCIS), pp. 129–131 (2010)Google Scholar
  18. 18.
    Wang, Z., et al.: Cluster Analysis Based on Spatial Feature Selecting in Spatial Data Mining, pp. 386–389. IEEE (2008)Google Scholar
  19. 19.
    Anselin, L., Schockaert, S., Smart, P.D., Twaroch, F.A.: Generating approximate region boundaries from heterogeneous spatial information: an evolutionary approach. J. Inf. Sci. 181(2),  257–283 (2011)Google Scholar
  20. 20.
    Shi, W.Z., Tong, X.H., Liu, D.J.: A least squares based method for adjusting the boundaries for area objects. Photogramm. Eng. Remote Sens. 71(2), 189–195 (2005)CrossRefGoogle Scholar
  21. 21.
    Wang, S, Yuan, H.: Spatial Data Mining in the Context of Big Data, pp. 486–492. IEEE (2013)Google Scholar
  22. 22.
    Zaiane, O.R., Lee, C.-H.: Clustering spatial data in the presence of obstacles: a density-based approach. In: Proceedings. International Database Engineering and Applications Symposium, pp. 214–223 (2002) Google Scholar
  23. 23.
    Perumal, M., Velumani, B.: Framework to find optimal cluster to place water purification plant using spatial clustering-DBSCAN. In: Materials Today Proceedings, vol. 25 (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer ApplicationsBharathiar UniversityCoimbatoreIndia

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