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Relational Mining in Spatial Domains: Accomplishments and Challenges

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6804))

Abstract

The rapid growth in the amount of spatial data available in Geographical Information Systems has given rise to substantial demand of data mining tools which can help uncover interesting spatial patterns. We advocate the relational mining approach to spatial domains, due to both various forms of spatial correlation which characterize these domains and the need to handle spatial relationships in a systematic way. We present some major achievements in this research direction and point out some open problems.

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References

  1. Anselin, L., Bera, A.: Spatial dependence in linear regression models with an application to spatial econometrics. In: Ullah, A., Giles, D. (eds.) Handbook of Applied Economics Statistics, pp. 21–74. Springer, Heidelberg (1998)

    Google Scholar 

  2. Appice, A., Berardi, M., Ceci, M., Malerba, D.: Mining and filtering multi-level spatial association rules with ARES. In: Hacid, M.-S., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds.) ISMIS 2005. LNCS (LNAI), vol. 3488, pp. 342–353. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  3. Appice, A., Ceci, M., Lanza, A., Lisi, F.A., Malerba, D.: Discovery of spatial association rules in georeferenced census data: A relational mining approach. Intelligent Data Analysis 7(6), 541–566 (2003)

    Google Scholar 

  4. Apice, A., Ceci, M., Malerba, D.: Mining model trees: A multi-relational approach. In: Horváth, T., Yamamoto, A. (eds.) ILP 2003. LNCS (LNAI), vol. 2835, pp. 4–21. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  5. Appice, A., Ceci, M., Malerba, D.: Transductive learning for spatial regression with co-training. In: Shin, S.Y., Ossowski, S., Schumacher, M., Palakal, M.J., Hung, C.-C. (eds.) SAC, pp. 1065–1070. ACM, New York (2010)

    Google Scholar 

  6. Ceci, M., Appice, A., Malerba, D.: Discovering emerging patterns in spatial databases: A multi-relational approach. In: Kok, J.N., Koronacki, J., de Mántaras, R.L., Matwin, S., Mladenic, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 390–397. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  7. Ceci, M., Appice, A., Malerba, D.: Transductive learning for spatial data classification. In: Koronacki, J., Ras, Z.W., Wierzchon, S.T., Kacprzyk, J. (eds.) Advances in Machine Learning I. Studies in Computational Intelligence, vol. 262, pp. 189–207. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  8. Chapelle, O., Schölkopf, B.B., Zien, A.: Semi-supervised learning. MIT Press, Cambridge (2006)

    Book  Google Scholar 

  9. Ciampi, A., Appice, A., Malerba, D.: Summarization for geographically distributed data streams. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds.) KES 2010. LNCS, vol. 6278, pp. 339–348. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  10. Densham, P.: Spatial decision support systems. Geographical Information Systems: Principles and Applications, 403–412 (1991)

    Google Scholar 

  11. Ester, M., Gundlach, S., Kriegel, H., Sander, J.: Database primitives for spatial data mining. In: Proceedings of the International Conference on Database in Office, Engineering and Science, BTW 1999, Freiburg, Germany (1999)

    Google Scholar 

  12. Frank, R., Ester, M., Knobbe, A.J.: A multi-relational approach to spatial classification. In: Elder IV, J.F., Fogelman-Soulié, F., Flach, P.A., Zaki, M.J. (eds.) KDD, pp. 309–318. ACM, New York (2009)

    Chapter  Google Scholar 

  13. Frank, R., Moser, F., Ester, M.: A method for multi-relational classification using single and multi-feature aggregation functions. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 430–437. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  14. Gao, X., Asami, Y., Chung, C.: An empirical evaluation of spatial regression models. Computers & Geosciences 32(8), 1040–1051 (2006)

    Article  Google Scholar 

  15. Getoor, L., Taskar, B. (eds.): Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)

    MATH  Google Scholar 

  16. Han, J., Kamber, M., Tung, A.K.H.: Spatial Clustering Methods in Data Mining: A Survey. In: Geographic Data Mining and Knowledge Discovery, pp. 1–29. Taylor and Francis, Abington (2001)

    Google Scholar 

  17. Jensen, D., Neville, J., Gallagher, B.: Why collective inference improves relational classification. In: Kim, W., Kohavi, R., Gehrke, J., DuMouchel, W. (eds.) KDD, pp. 593–598. ACM, New York (2004)

    Google Scholar 

  18. Klösgen, W., May, M.: Spatial subgroup mining integrated in an object-relational spatial database. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 275–286. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  19. Koperski, K., Han, J.: Discovery of spatial association rules in geographic information databases. In: Egenhofer, M.J., Herring, J.R. (eds.) SSD 1995. LNCS, vol. 951, pp. 47–66. Springer, Heidelberg (1995)

    Chapter  Google Scholar 

  20. Kühn, I.: Incorporating spatial autocorrelation invert observed patterns. Diversity and Distributions 13(1), 66–69 (2007)

    Google Scholar 

  21. LeSage, J.P., Pace, K.: Spatial dependence in data mining. In: Grossman, R., Kamath, C., Kegelmeyer, P., Kumar, V., Namburu, R. (eds.) Data Mining for Scientific and Engineering Applications, pp. 439–460. Kluwer Academic Publishing, Dordrecht (2001)

    Chapter  Google Scholar 

  22. Lisi, F.A., Malerba, D.: Inducing multi-level association rules from multiple relations. Machine Learning 55, 175–210 (2004)

    Article  MATH  Google Scholar 

  23. Malerba, D.: A relational perspective on spatial data mining. IJDMMM 1(1), 103–118 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  24. Malerba, D., Ceci, M., Appice, A.: Mining model trees from spatial data. In: Jorge, A., Torgo, L., Brazdil, P., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 169–180. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  25. Malerba, D., Esposito, F., Lanza, A., Lisi, F.A., Appice, A.: Empowering a GIS with inductive learning capabilities: The case of INGENS. Journal of Computers, Environment and Urban Systems 27, 265–281 (2003)

    Article  Google Scholar 

  26. Pekerskaya, I., Pei, J., Wang, K.: Mining changing regions from access-constrained snapshots: a cluster-embedded decision tree approach. Journal of Intelligent Information Systems 27(3), 215–242 (2006)

    Article  Google Scholar 

  27. Samet, H.: Applications of spatial data structures. Addison-Wesley, Longman (1990)

    Google Scholar 

  28. Sander, J., Ester, M., Kriegel, H., Xu, X.: Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications. Data Mining and Knowledge Discovery 2(2), 169–194 (1998)

    Article  Google Scholar 

  29. Seeger, M.: Learning with labeled and unlabeled data. Technical report, University of Edinburgh (2001)

    Google Scholar 

  30. Shekhar, S., Chawla, S.: Spatial databases: A tour. Prentice Hall, Upper Saddle River (2003)

    Google Scholar 

  31. Shekhar, S., Huang, Y., Wu, W., Lu, C.: What’s spatial about spatial data mining: Three case studies. In: Grossman, R., Kamath, C., Kegelmeyer, P., Kumar, V., Namburu, R. (eds.) Data Mining for Scientific and Engineering Applications. Massive Computing, vol. 2, pp. 357–380. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  32. Shekhar, S., Schrater, P.R., Vatsavai, R.R., Wu, W., Chawla, S.: Spatial contextual classification and prediction models for mining geospatial data. IEEE Transactions on Multimedia 4(2), 174–188 (2002)

    Article  Google Scholar 

  33. Shekhar, S., Vatsavai, R., Chawla, S.: Spatial classification and prediction models for geospatial data mining. In: Miller, H., Han, J. (eds.) Geographic Data Mining and Knowledge Discovery, 2nd edn., pp. 117–147. Taylor & Francis, Abington (2009)

    Google Scholar 

  34. Shekhar, S., Zhang, P., Huang, Y.: Spatial data mining. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 837–854. Springer, Heidelberg (2010)

    Google Scholar 

  35. Tobler, W.: A computer movie simulating urban growth in the detroit region. Economic Geography 46, 234–240 (1970)

    Article  Google Scholar 

  36. Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)

    MATH  Google Scholar 

  37. Vert, G., Alkhaldi, R., Nasser, S., Harris Jr., F.C., Dascalu, S.M.: A taxonomic model supporting high performance spatial-temporal queries in spatial databases. In: Proceedings of High Performance Computing Systems (HPCS 2007), pp. 810–816 (2007)

    Google Scholar 

  38. Wrobel, S.: An algorithm for multi-relational discovery of subgroups. In: Komorowski, J., Żytkow, J.M. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 78–87. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  39. Yin, X., Han, J., Yang, J., Yu, P.S.: CrossMine: Efficient classification across multiple database relations. In: ICDE, pp. 399–411. IEEE Computer Society, Los Alamitos (2004)

    Google Scholar 

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Malerba, D., Ceci, M., Appice, A. (2011). Relational Mining in Spatial Domains: Accomplishments and Challenges. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2011. Lecture Notes in Computer Science(), vol 6804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21916-0_2

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  • DOI: https://doi.org/10.1007/978-3-642-21916-0_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21915-3

  • Online ISBN: 978-3-642-21916-0

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