Skip to main content
Springer Nature Link
Log in

SpaGRID: A Spatial Grid Framework for High Dimensional Medical Databases

  • Conference paper
Hybrid Artificial Intelligent Systems (HAIS 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7208))

Included in the following conference series:

  • 1819 Accesses

Abstract

The outgrowth of technology in geographical databases has enhanced the growth of spatial databases, to deal with such enlarging databases scientists are laying down enormous efforts that can efficiently process these databases. Spatial data mining techniques has been collaboratively applied to extract implicit knowledge from spatial as well as non-spatial attributes. These techniques are efficiently applied in several fields such as healthcare, environmental, marketing and remote sensing databases to improve planning and decision making process. In this paper, we have designed and implemented SpaGRID framework for detection of spatial clusters. The framework has unprecedented efficiency to extract implicit knowledge of spatial data, due to its accessibility to handle and discover hidden patterns from spatial databases. We have also illustrated the usage of spatial variations among the United States men with prevalence of prostate cancer disease. The data of age group was taken from (15-65+) years in this group prostate cancers were examined and several stages of disease diagnosis was taken into account. The population of data was characterized by white, black and others were too small to be taken into account. Numerous challenges were encountered due to complexity of spatial datasets hence being resolved by certain statistical measures. The approach is to discover knowledge from spatial databases and design different aspects of knowledge discovery process from spatial databases.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. In: Proceedings of the 1998 ACM-SIGMOD International Conference on Management of Data, Seattle, Washington, pp. 94–105 (June 1998)

    Google Scholar 

  2. Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: OPTICS: Ordering Points To Identify the Clustering Structure. In: Proceedings of the ACM SIGMOD Conference, Philadelphia, PA, USA, pp. 49–60 (1999)

    Google Scholar 

  3. Borah, B., Bhattacharyya, D.K.: An Improved Sampling-based DBSCAN for Large Spatial Databases. In: Proceedings of the International Conference on Intelligent Sensing and Information, p. 92 (2004)

    Google Scholar 

  4. Xu, X., Ester, M., Kriegel, H.-P., Sander, J.: A Distribution-Based Clustering Algorithm for Mining in Large Spatial Databases. In: Proceedings of the International Conference on Data Engineering (ICDE 1998), Orlando, FL, pp. 324–331. AAAIPress (1998)

    Google Scholar 

  5. Giha, S., Rastogi, R., Shim, K.: CURE: an efficient clustering algorithm for large databases. In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, pp.73–84 (June 1998)

    Google Scholar 

  6. Goil, S., Nagesh, H., Choudhary, A.: MAFIA: Efficient and Scalable Clustering for very large data sets. Technical Report No. CPDC – TR – 9906 – 010 ©1999 Center for Parallel and distributed Computing (June 1999)

    Google Scholar 

  7. Guha, S., Rastogi, R., Shim, K.: ROCK: A Robust Clustering Algorithm for Categorical Attributes. In: Proceedings of the 15th International Conference on Data Engineering, pp. 512–521 (March 1999)

    Google Scholar 

  8. Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: an efficient data clustering method for very large databases. In: Proceedings of SIGMOD International Conference, pp. 103–114 (1996)

    Google Scholar 

  9. Hinneburg, A., Keim, A.D.: An Efficient Approach to Clustering in Large Multimedia Databases with Noise. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (KDD 1998), New York, NY, USA, pp. 58–65 (August 1998)

    Google Scholar 

  10. Kaur, H., Wasan, S.K.: An Integrated Approach in Medical Decision Making for Eliciting Knowledge, Web-based Applications in Health Care & Biomedicine. In: Lazakidou, A. (ed.) Annals of Information Systems (AoIS). Springer, Heidelberg (2009)

    Google Scholar 

  11. Karypis, G., Eui-Hong, H., Kumar, V.: CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling. IEEE Computer 32(8), 68–75 (1999)

    Article  Google Scholar 

  12. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster in Analysis. John Wiley and Sons (1990)

    Google Scholar 

  13. Ng, R.T., Han, J.: Efficient and Effective clustering methods for spatial data mining. In: Bocca, J., Jarke, M., Zaniolo, C. (eds.) 20th International Conference on Very Large Data Bases, pp. 4–155. Morgan Kaufmann Publishers, USA (1994)

    Google Scholar 

  14. Miller, H.J., Han, J.: Geographic data mining and knowledge discovery. Taylor and Francis (2001)

    Google Scholar 

  15. Sheikholeslami, G., Chatterjee, S., Zhang, A.: WaveCluster: A Multi Resolution Clustering Approach for Very Large Spatial Databases. In: Proceedings of 24th Very Large Databases Conference (VLDB 1998), New York, NY, USA, pp. 428–439 (1998)

    Google Scholar 

  16. Wang, W., Yang, J., Muntz, R.: STING: A Statistical Information Grid Approach to Spatial Data Mining. In: Proceedings of the 23rd VLDB Conference, Athens, Greece, pp. 186–195 (1997)

    Google Scholar 

  17. Laurini, R., Thompson, D.: Fundamentals of spatial Information systems. Academic Press (1992)

    Google Scholar 

  18. Cressie, N.: Statistics for a Spatial Data, revised edn. Wiley, NY (1990)

    Google Scholar 

  19. Chauhan, R., Kaur, H., Alam, M.A.: Data Clustering Method for discovering clusters in Spatial Cancer Databases. International Journal of Computer Applications (2010)

    Google Scholar 

  20. Kaur, H., Chauhan, R., Alam, M.A.: An Optimal Categorization of Feature Selection Methods for Knowledge Discovery. In: Zhang, Segall, Cao (eds.) Visual Analytics and Interactive Technologies:Data, Text and Web Mining Applications. IGI Publishers Inc. (2010)

    Google Scholar 

  21. Seeger, B., Kriegel, H.P.: Techniques for design and implementation of spatial access methods. In: Proceedings of 14th International Conference on Very Large Databases, pp. 360–371 (1988)

    Google Scholar 

  22. Kohavi, R., John, G.: Wrappers for feature subset election. Artificial Intelligence 1-2, 273–324 (1997)

    Article  Google Scholar 

  23. Dash, M., Liu, H.: Feature selection methods for classifications. Intelligent Data Analysis. An International Journal 1, 131–156 (1997)

    Article  Google Scholar 

  24. Langley, P.: Selection of relevant features in machine learning. In: Proceedings of the AAAI Fall Symposium on Relevance. AAAI Press, New Orleans (1994)

    Google Scholar 

  25. Peucker, T.K., Chrisman, N.: Cartographic data structures. American Cartographer 2, 55–69 (1975)

    Article  Google Scholar 

  26. Overmars, M.H., Leeuwen, J.V.: Dynamic multi-dimensional data structures based on quad tree and k-d-trees. Acta Informatica 17(3), 267–285 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  27. Blum, A.L., Langley, P.: Selection of relevant features and examples in machine learning. Artificial Intelligence 97, 245–271 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  28. Dy, J.G., Brodley, C.E.: Feature Subset Selection and Order Identification for Unsupervised Learning. In: Proceedings of the 17th International Conference on Machine Learning, pp. 247–254. Stanford University, CA (2000)

    Google Scholar 

  29. He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. In: NIPS, pp. 507–514 (2005)

    Google Scholar 

  30. Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Machine Learning 46, 389–422 (2002)

    Article  MATH  Google Scholar 

  31. Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, Norwell (1998)

    Book  MATH  Google Scholar 

  32. Zhang, C., Murayama, Y.: Testing local spatial autocorrelation using k-order neighbors. International Journal of Geographical Information Science 14, 681–692 (2000)

    Article  Google Scholar 

  33. Kang, I.S., Kim, T.W., Li, K.J.: A spatial data mining method by Delaunay triangulation. In: The 5th International Workshop on Advances in Geographic Information Systems, LasVegas, Nevada (1997)

    Google Scholar 

  34. Harel, D., Koren, Y.: Clustering spatial data using random walks. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California (2001)

    Google Scholar 

  35. Corchado, E., Graña, M., Wozniak, M.: New trends and applications on hybrid artificial intelligence systems. Neurocomputing 75(1), 61–63 (2012)

    Article  Google Scholar 

  36. Mann, S., Benwell, G.L.: The integration of ecological, neural and spatial modeling for monitoring and prediction for semi-arid landscapes. Computers and Geosciences 22(9), 1003–1012 (1996)

    Article  Google Scholar 

  37. Jacquez, G.: Spatial analysis in epidemiology: Nascent science or a failure of GIS? Journal of Geographical Systems 2, 91–97 (2000)

    Article  Google Scholar 

  38. Corchado, E., Abraham, A., Carvalho, A.: Hybrid intelligent algorithms and applications. Information Sciences 180(14), 2633–2634 (2010)

    Article  MathSciNet  Google Scholar 

  39. http://fha.maryland.gov

  40. Cohen, J., Cohen, P.: Applied multiple regression/correlation analysis for the behavioral sciences, 2nd edn. Erlbaum, Hillsdale (1983)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. International Institute for Global Health, United Nations University, Kuala Lumpur, Malaysia

    Harleen Kaur, Syed Aljunid & Mohd. Salleh

  2. Dept. of Computer Science, Hamdard University, New Delhi, India

    Ritu Chauhan & Mohd. Afshar Alam

Authors
  1. Harleen Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ritu Chauhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohd. Afshar Alam
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Syed Aljunid
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohd. Salleh
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Universidad de Salamanca, Plaza de la Merced S/N, 37008, Salamanca, Spain

    Emilio Corchado

  2. VŠB-TU Ostrava 17, Listopadu 15, 70833, Ostrava, Czech Republic

    Václav Snášel

  3. Machine Intelligence Research Labs(MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071, Auburn, Washington, USA

    Ajith Abraham

  4. Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michał Woźniak

  5. University of the Basque Country, Pº Manuel Lardizabal 1, 20018, San Sebastian, Spain

    Manuel Graña

  6. Yonsei University, 134 Shinchon-dong, Sudaemoon-ku, 120-749, Seoul, Korea

    Sung-Bae Cho

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kaur, H., Chauhan, R., Alam, M.A., Aljunid, S., Salleh, M. (2012). SpaGRID: A Spatial Grid Framework for High Dimensional Medical Databases. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28942-2_62

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28942-2_62

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28941-5

  • Online ISBN: 978-3-642-28942-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics