Skip to main content
SpringerLink
Log in
Book cover

International Conference: Beyond Databases, Architectures and Structures

BDAS 2017: Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation pp 115–126Cite as

A Survey on Data Mining Methods for Clustering Complex Spatiotemporal Data

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 716))

Abstract

This publication presents a survey on the clustering algorithms proposed for spatiotemporal data. We begin our study with definitions of spatiotemporal datatypes. Next we provide a categorization of spatiotemporal datatypes with the special emphasis on the spatial representation and diversity in temporal aspect. We conduct our deliberation focusing mainly on the complex spatiotemporal objects. In particular, we review algorithms for two problems already proposed in literature: clustering complex spatiotemporal objects as polygons or geographical areas and measuring distances between complex spatial objects. In addition to description of the problems mentioned above, we also attempt to provide a comprehensive references review and provide a general look on the different problems related to the clustering spatiotemporal data.

This is a preview of subscription content, 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   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

References

  1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB 1994, pp. 487–499. Morgan Kaufmann Publishers Inc., San Francisco (1994)

    Google Scholar 

  2. Alt, H., Behrends, B., Blömer, J.: Approximate matching of polygonal shapes. Ann. Math. Artif. Intell. 13(3), 251–265 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  3. Alt, H., Godau, M.: Computing the frÉchet distance between two polygonal curves. Int. J. Comput. Geom. Appl. 05(01n02), 75–91 (1995)

    Article  MATH  Google Scholar 

  4. Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: OPTICS: ordering points to identify the clustering structure. In: Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, SIGMOD 1999, pp. 49–60. ACM, New York (1999)

    Google Scholar 

  5. Atallah, M.J.: A linear time algorithm for the hausdorff distance between convex polygons. Inf. Process. Lett. 17(4), 207–209 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  6. Aydin, B., Angryk, R.: Spatiotemporal frequent pattern mining on solar data: current algorithms and future directions. In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pp. 575–581, November 2015

    Google Scholar 

  7. Bazan, J.G.: Hierarchical classifiers for complex spatio-temporal concepts. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 474–750. Springer, Heidelberg (2008). doi:10.1007/978-3-540-89876-4_26

    Chapter  Google Scholar 

  8. Benkert, M., Gudmundsson, J., Hübner, F., Wolle, T.: Reporting flock patterns. Comput. Geom. 41(3), 111–125 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  9. Birant, D., Kut, A.: ST-DBSCAN: an algorithm for clustering spatial-temporal data. Data Knowl. Eng. 60(1), 208–221 (2007). Intelligent Data Mining

    Article  Google Scholar 

  10. Buchin, K., Buchin, M., Wenk, C.: Computing the fréchet distance between simple polygons. Comput. Geom. 41(1–2), 2–20 (2008). special Issue on the 22nd European Workshop on Computational Geometry (EuroCG)22nd European Workshop on Computational Geometry

    Article  MathSciNet  MATH  Google Scholar 

  11. Chan, K.P., Fu, A.W.C.: Efficient time series matching by wavelets. In: Proceedings 15th International Conference on Data Engineering (Cat. No. 99CB36337), pp. 126–133, March 1999

    Google Scholar 

  12. Chen, C.-S., Eick, C.F., Rizk, N.J.: Mining spatial trajectories using non-parametric density functions. In: Perner, P. (ed.) MLDM 2011. LNCS (LNAI), vol. 6871, pp. 496–510. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23199-5_37

    Chapter  Google Scholar 

  13. Chen, L., Ng, R.: On the marriage of Lp-norms and edit distance. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases, VLDB 2004, vol. 30, pp. 792–803. VLDB Endowment (2004)

    Google Scholar 

  14. Chen, L., Özsu, M.T., Oria, V.: Robust and fast similarity search for moving object trajectories. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, SIGMOD 2005, pp. 491–502. ACM, New York (2005)

    Google Scholar 

  15. Damiani, M.L., Issa, H., Fotino, G., Heurich, M., Cagnacci, F.: Introducing ‘presence’ and ‘stationarity index’ to study partial migration patterns: an application of a spatio-temporal clustering technique. Int. J. Geogr. Inf. Sci. 30(5), 907–928 (2016)

    Article  Google Scholar 

  16. Eiter, T., Mannila, H.: Computing discrete fréchet distance. Technical report, Vienna University of Technology (1994)

    Google Scholar 

  17. Erwig, M., Güting, R.H., Schneider, M., Vazirgiannis, M.: Spatio-temporal data types: an approach to modeling and querying moving objects in databases. GeoInformatica 3(3), 269–296 (1999)

    Article  Google Scholar 

  18. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Second International Conference on Knowledge Discovery and Data Mining, pp. 226–231. AAAI Press (1996)

    Google Scholar 

  19. Estivill-Castro, V., Lee, I.: Autoclust: automatic clustering via boundary extraction for mining massive point-data sets. In: Proceedings of the 5th International Conference on Geocomputation, pp. 23–25 (2000)

    Google Scholar 

  20. Gora, P., Rüb, I.: Traffic models for self-driving connected cars. Transp. Res. Procedia 14, 2207–2216 (2016). Transport Research Arena (TRA 2016)

    Article  Google Scholar 

  21. Gudmundsson, J., van Kreveld, M.: Computing longest duration flocks in trajectory data. In: Proceedings of the 14th Annual ACM International Symposium on Advances in Geographic Information Systems, GIS 2006, pp. 35–42. ACM, New York (2006)

    Google Scholar 

  22. Han, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco (2005)

    Google Scholar 

  23. Huang, Y., Zhang, L., Zhang, P.: A framework for mining sequential patterns from spatio-temporal event data sets. IEEE Trans. Knowl. Data Eng. 20(4), 433–448 (2008)

    Article  Google Scholar 

  24. Iyengar, V.S.: On detecting space-time clusters. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 587–592. ACM (2004)

    Google Scholar 

  25. Izakian, H., Pedrycz, W.: Anomaly detection and characterization in spatial time series data: a cluster-centric approach. IEEE Trans. Fuzzy Syst. 22(6), 1612–1624 (2014)

    Article  Google Scholar 

  26. Izakian, H., Pedrycz, W., Jamal, I.: Clustering spatiotemporal data: an augmented fuzzy c-means. IEEE Trans. Fuzzy Syst. 21(5), 855–868 (2013)

    Article  Google Scholar 

  27. Izakian, H., Pedrycz, W.: A new PSO-optimized geometry of spatial and spatio-temporal scan statistics for disease outbreak detection. Swarm Evol. Comput. 4, 1–11 (2012)

    Article  Google Scholar 

  28. Jeung, H., Yiu, M.L., Zhou, X., Jensen, C.S., Shen, H.T.: Discovery of convoys in trajectory databases. Proc. VLDB Endow. 1(1), 1068–1080 (2008)

    Article  Google Scholar 

  29. Joshi, D., Samal, A., Soh, L.K.: A dissimilarity function for clustering geospatial polygons. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2009, pp. 384–387. ACM, New York (2009)

    Google Scholar 

  30. Joshi, D., Samal, A., Soh, L.K.: Spatio-temporal polygonal clustering with space and time as first-class citizens. Geoinformatica 17(2), 387–412 (2013)

    Article  Google Scholar 

  31. Kasabov, N., Capecci, E.: Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes. Inf. Sci. 294, 565–575 (2015). Innovative Applications of Artificial Neural Networks in Engineering

    Article  MathSciNet  Google Scholar 

  32. Kasabov, N., Scott, N.M., Tu, E., Marks, S., Sengupta, N., Capecci, E., Othman, M., Doborjeh, M.G., Murli, N., Hartono, R., Espinosa-Ramos, J.I., Zhou, L., Alvi, F.B., Wang, G., Taylor, D., Feigin, V., Gulyaev, S., Mahmoud, M., Hou, Z.G., Yang, J.: Evolving spatio-temporal data machines based on the neucube neuromorphic framework: design methodology and selected applications. Neural Netw. 78, 1–14 (2016). special Issue on “Neural Network Learning in Big Data”

    Article  Google Scholar 

  33. Keogh, E., Ratanamahatana, C.A.: Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7(3), 358–386 (2005)

    Article  Google Scholar 

  34. Kisilevich, S., Mansmann, F., Nanni, M., Rinzivillo, S.: Spatio-temporal clustering. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 855–874. Springer, Boston (2010)

    Google Scholar 

  35. Kryszkiewicz, M., Lasek, P.: TI-DBSCAN: clustering with DBSCAN by means of the triangle inequality. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.) RSCTC 2010. LNCS (LNAI), vol. 6086, pp. 60–69. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13529-3_8

    Chapter  Google Scholar 

  36. Kulldorff, M.: A spatial scan statistic. Commun. Stat. Theory Methods 26(6), 1481–1496 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  37. Lee, J.G., Han, J., Whang, K.Y.: Trajectory clustering: a partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, SIGMOD 2007, pp. 593–604. ACM, New York (2007)

    Google Scholar 

  38. Li, L., Revesz, P.: A comparison of spatio-temporal interpolation methods. In: Egenhofer, M.J., Mark, D.M. (eds.) GIScience 2002. LNCS, vol. 2478, pp. 145–160. Springer, Heidelberg (2002). doi:10.1007/3-540-45799-2_11

    Chapter  Google Scholar 

  39. Li, Z.: Spatiotemporal pattern mining: algorithms and applications. In: Aggarwal, C.C., Han, J. (eds.) Frequent Pattern Mining, pp. 283–306. Springer, Cham (2014). doi:10.1007/978-3-319-07821-2_12

    Google Scholar 

  40. Li, Z., Ding, B., Han, J., Kays, R.: Swarm: mining relaxed temporal moving object clusters. Proc. VLDB Endow. 3(1–2), 723–734 (2010)

    Article  Google Scholar 

  41. Mohan, P., Shekhar, S., Shine, J.A., Rogers, J.P.: Cascading spatio-temporal pattern discovery. IEEE Trans. Knowl. Data Eng. 24(11), 1977–1992 (2012)

    Article  Google Scholar 

  42. Moon, T.K.: The expectation-maximization algorithm. IEEE Sig. Process. Mag. 13(6), 47–60 (1996)

    Article  Google Scholar 

  43. Nanni, M., Pedreschi, D.: Time-focused clustering of trajectories of moving objects. J. Intell. Inf. Syst. 27(3), 267–289 (2006)

    Article  Google Scholar 

  44. Ng, R.T., Han, J.: CLARANS: a method for clustering objects for spatial data mining. IEEE Trans. Knowl. Data Eng. 14(5), 1003–1016 (2002)

    Article  Google Scholar 

  45. Palma, A.T., Bogorny, V., Kuijpers, B., Alvares, L.O.: A clustering-based approach for discovering interesting places in trajectories. In: Proceedings of the 2008 ACM Symposium on Applied Computing, SAC 2008, pp. 863–868. ACM, New York (2008)

    Google Scholar 

  46. Schubert, E., Zimek, A., Kriegel, H.P.: Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection. Data Min. Knowl. Disc. 28(1), 190–237 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  47. Shekhar, S., Evans, M.R., Kang, J.M., Mohan, P.: Identifying patterns in spatial information: a survey of methods. Wiley Interdisc. Rev.: Data Mining Knowl. Discov. 1(3), 193–214 (2011)

    Google Scholar 

  48. Tork, H.F.: Spatio-temporal clustering methods classification. In: Doctoral Symposium on Informatics Engineering, vol. 1, no. 1, pp. 199–209. FEUP (2012)

    Google Scholar 

  49. Wang, M., Wang, A., Li, A.: Mining spatial-temporal clusters from geo-databases. In: Li, X., Zaïane, O.R., Li, Z. (eds.) ADMA 2006. LNCS (LNAI), vol. 4093, pp. 263–270. Springer, Heidelberg (2006). doi:10.1007/11811305_29

    Chapter  Google Scholar 

  50. Wang, S., Cai, T., Eick, C.F.: New spatiotemporal clustering algorithms and their applications to ozone pollution. In: Proceedings of the 2013 IEEE 13th International Conference on Data Mining Workshops, ICDMW 2013, pp. 1061–1068. IEEE Computer Society, Washington, DC (2013)

    Google Scholar 

  51. Wang, W., Du, S., Guo, Z., Luo, L.: Polygonal clustering analysis using multilevel graph-partition. Trans. GIS 19(5), 716–736 (2015)

    Article  Google Scholar 

  52. Yi, B.K., Jagadish, H.V., Faloutsos, C.: Efficient retrieval of similar time sequences under time warping. In: Proceedings of the Fourteenth International Conference on Data Engineering, ICDE 1998, pp. 201–208. IEEE Computer Society, Washington, DC (1998)

    Google Scholar 

  53. Zhang, Y., Eick, C.F.: Novel clustering and analysis techniques for mining spatio-temporal data. In: Proceedings of the 1st ACM SIGSPATIAL PhD Workshop, SIGSPATIAL PhD 2014, pp. 2:1–2:5. ACM, New York (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665, Warsaw, Poland

    Piotr S. Maciąg

Authors
  1. Piotr S. Maciąg
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Piotr S. Maciąg .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Dariusz Mrozek

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Paweł Kasprowski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Bożena Małysiak-Mrozek

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Daniel Kostrzewa

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Maciąg, P.S. (2017). A Survey on Data Mining Methods for Clustering Complex Spatiotemporal Data. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation. BDAS 2017. Communications in Computer and Information Science, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-319-58274-0_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-58274-0_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58273-3

  • Online ISBN: 978-3-319-58274-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation