A Survey on Data Mining Methods for Clustering Complex Spatiotemporal Data

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


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.


Data mining Clustering spatiotemporal data Clustering algorithms 


  1. 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. 2.
    Alt, H., Behrends, B., Blömer, J.: Approximate matching of polygonal shapes. Ann. Math. Artif. Intell. 13(3), 251–265 (1995)MathSciNetMATHCrossRefGoogle Scholar
  3. 3.
    Alt, H., Godau, M.: Computing the frÉchet distance between two polygonal curves. Int. J. Comput. Geom. Appl. 05(01n02), 75–91 (1995)MATHCrossRefGoogle Scholar
  4. 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. 5.
    Atallah, M.J.: A linear time algorithm for the hausdorff distance between convex polygons. Inf. Process. Lett. 17(4), 207–209 (1983)MathSciNetMATHCrossRefGoogle Scholar
  6. 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 2015Google Scholar
  7. 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 CrossRefGoogle Scholar
  8. 8.
    Benkert, M., Gudmundsson, J., Hübner, F., Wolle, T.: Reporting flock patterns. Comput. Geom. 41(3), 111–125 (2008)MathSciNetMATHCrossRefGoogle Scholar
  9. 9.
    Birant, D., Kut, A.: ST-DBSCAN: an algorithm for clustering spatial-temporal data. Data Knowl. Eng. 60(1), 208–221 (2007). Intelligent Data MiningCrossRefGoogle Scholar
  10. 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 GeometryMathSciNetMATHCrossRefGoogle Scholar
  11. 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 1999Google Scholar
  12. 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 CrossRefGoogle Scholar
  13. 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. 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. 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)CrossRefGoogle Scholar
  16. 16.
    Eiter, T., Mannila, H.: Computing discrete fréchet distance. Technical report, Vienna University of Technology (1994)Google Scholar
  17. 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)CrossRefGoogle Scholar
  18. 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. 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. 20.
    Gora, P., Rüb, I.: Traffic models for self-driving connected cars. Transp. Res. Procedia 14, 2207–2216 (2016). Transport Research Arena (TRA 2016)CrossRefGoogle Scholar
  21. 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. 22.
    Han, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco (2005)Google Scholar
  23. 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)CrossRefGoogle Scholar
  24. 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. 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)CrossRefGoogle Scholar
  26. 26.
    Izakian, H., Pedrycz, W., Jamal, I.: Clustering spatiotemporal data: an augmented fuzzy c-means. IEEE Trans. Fuzzy Syst. 21(5), 855–868 (2013)CrossRefGoogle Scholar
  27. 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)CrossRefGoogle Scholar
  28. 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)CrossRefGoogle Scholar
  29. 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. 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)CrossRefGoogle Scholar
  31. 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 EngineeringMathSciNetCrossRefGoogle Scholar
  32. 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”CrossRefGoogle Scholar
  33. 33.
    Keogh, E., Ratanamahatana, C.A.: Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7(3), 358–386 (2005)CrossRefGoogle Scholar
  34. 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. 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 CrossRefGoogle Scholar
  36. 36.
    Kulldorff, M.: A spatial scan statistic. Commun. Stat. Theory Methods 26(6), 1481–1496 (1997)MathSciNetMATHCrossRefGoogle Scholar
  37. 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. 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 CrossRefGoogle Scholar
  39. 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. 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)CrossRefGoogle Scholar
  41. 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)CrossRefGoogle Scholar
  42. 42.
    Moon, T.K.: The expectation-maximization algorithm. IEEE Sig. Process. Mag. 13(6), 47–60 (1996)CrossRefGoogle Scholar
  43. 43.
    Nanni, M., Pedreschi, D.: Time-focused clustering of trajectories of moving objects. J. Intell. Inf. Syst. 27(3), 267–289 (2006)CrossRefGoogle Scholar
  44. 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)CrossRefGoogle Scholar
  45. 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. 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)MathSciNetMATHCrossRefGoogle Scholar
  47. 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. 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. 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 CrossRefGoogle Scholar
  50. 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. 51.
    Wang, W., Du, S., Guo, Z., Luo, L.: Polygonal clustering analysis using multilevel graph-partition. Trans. GIS 19(5), 716–736 (2015)CrossRefGoogle Scholar
  52. 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. 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

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Computer ScienceWarsaw University of TechnologyWarsawPoland

Personalised recommendations