Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Advances in Big Data and Cloud Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 645))

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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. 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)

    Article  Google Scholar 

  3. Wang, S., Ding, G., Zhong, M.: Big Spatial Data Mining, pp. 13–21. IEEE (2013)

    Google Scholar 

  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)

    Article  Google Scholar 

  5. Bi, S., et al.: Spatial Data Mining in Settlement Archaeological Databases Based on Vector Features, pp. 277–281. IEEE (2008)

    Google Scholar 

  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. 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. 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. He, B., Fang, T., Guo, D.: Uncertainty in Spatial Data Mining, pp. 1152–1156. IEEE (2004)

    Google Scholar 

  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. 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. Yasmin, M.: Dynamic Referencing Rules Creation Using Intelligent Agent in Geo Spatial Data Mining. IEEE (2012)

    Google Scholar 

  13. Shengwu, H.: Method Development about Spatial Data Mining and Its Problem Analysis, vol. 2, pp. 144–147. IEEE (2011)

    Google Scholar 

  14. Shekhar, S., Lu, C.T., Zhang, P.: A unified approach to detection spatial outliers. GeoInformatica 7, 139–166 (2003)

    Article  Google Scholar 

  15. Peng, S., Fang, J., Han, C., Cheng, Z.: VegaMinerPOI: A Spatial Data Mining System for POI Datasets, pp. 1–4

    Google Scholar 

  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. 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. Wang, Z., et al.: Cluster Analysis Based on Spatial Feature Selecting in Spatial Data Mining, pp. 386–389. IEEE (2008)

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  21. Wang, S, Yuan, H.: Spatial Data Mining in the Context of Big Data, pp. 486–492. IEEE (2013)

    Google Scholar 

  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. 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 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Applications, Bharathiar University, Coimbatore, India

    Mousi Perumal & Bhuvaneswari Velumani

Authors
  1. Mousi Perumal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bhuvaneswari Velumani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mousi Perumal .

Editor information

Editors and Affiliations

  1. Department of Computer Sciences Technology, Karunya University, Coimbatore, Tamil Nadu, India

    Elijah Blessing Rajsingh

  2. Department of Computer Science, Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, Texas, USA

    Jey Veerasamy

  3. Department of Civil and Environmental Engineering, University of Missouri, Columbia, Missouri, USA

    Amir H. Alavi

  4. Department of Computer Sciences Technology, Karunya University, Coimbatore, Tamil Nadu, India

    J. Dinesh Peter

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Perumal, M., Velumani, B. (2018). Design and Development of Hybridized DBSCAN-NN Approach for Location Prediction to Place Water Treatment Plant. In: Rajsingh, E., Veerasamy, J., Alavi, A., Peter, J. (eds) Advances in Big Data and Cloud Computing. Advances in Intelligent Systems and Computing, vol 645. Springer, Singapore. https://doi.org/10.1007/978-981-10-7200-0_21

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-7200-0_21

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7199-7

  • Online ISBN: 978-981-10-7200-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation