Abstract
Researchers from government offices, educational institutes and private organizations are generating large amounts of geospatial data. Even though they provide valuable information itself, there is a growing need for analysis of these data to obtain new insights. Many organizations are applying traditional data mining techniques to geospatial data in order to get information that is even more valuable. Here we will overview five techniques of data mining adapted to work with geographic information systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
H.J. Miller, J. Han, Geographic data mining and knowledge discovery, 2nd edn. Geogr. Data Min. Knowl. Discov. 1–475 (2009). https://doi.org/10.1201/9781420073980
R. Tomlinson, Geographical Information Systems, Spatial Data Analysis and Decision Making in Government (University of London, 1974)
P. Folger, Geospatial information and geographic information systems (GIS): an overview for congress. Fed. Geospatial Inf. Manage. Coord. pp. 1–19 (2013)
B. Tomaszewski, Geographic Information Systems (GIS) for Disaster Management (Boca Raton, FL, 2014)
U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, From Data Mining to Knowledge Discovery in Databases, in Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligent Lecture Notes Bioinformatics), vol. 9078, no. 3, pp. 637–648 (2015). https://doi.org/10.1007/978-3-319-18032-8_50
M.-S. Chen, J. Han, P.S. Yu, Data Mining: an overview from database perspective. IEEE Trans. Knowl. Data Eng. 8(6), 866–883 (1997)
H. Sahu, S. Shrma, S. Gondhalakar, A brief overview on data mining survey. IJCTEE 1(3), 114–121 (2008)
C. Wan, Data Mining Tasks and Paradigms, in Hierarchical Feature Selection for Knowledge Discovery: Application of Data Mining to the Biology of Ageing (Springer International Publishing, Cham, 2019), pp. 7–15
K. Zeitouni, A survey of spatial data mining methods databases and statistics point of views. Data Warehous. Web Eng. (2011). https://doi.org/10.4018/9781931777025.ch013
S. Shekhar, M.R. Evans, J.M. Kang, P. Mohan, Identifying patterns in spatial information: a survey of methods. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 1(3), 193–214 (2011). https://doi.org/10.1002/widm.25
S. Shekhar, C.-T. Lu, P. Zhang, Detecting graph-based spatial outliers: algorithms and applications (a summary of results), in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2001)
Y. Kou, C.-T. Lu, Outlier Detection, Spatial, in Encyclopedia of GIS. ed. by S. Shekhar, H. Xiong, X. Zhou (Springer International Publishing, Cham, 2017), pp. 1539–1546
K. Koperski, J. Adhikary, J. Han, Spatial data mining: progress and challenges survey paper. Phys. Earth Planet. Inter. 5(C), 7 (1972). https://doi.org/10.1016/0031-9201(72)90070-2
D. Malerba, F.A. Lisi, An ILP method for spatial association rule mining. Multi-Relational Data Mining (2001)
P. Berkhin, A survey of clustering data mining techniques, in Grouping Multidimensional Data: Recent Advances in Clustering, eds. by J. Kogan, C. Nicholas, M. Teboulle (Springer, Berlin, Heidelberg, 2006), pp. 25–71
S. Kumar, S. Ramulu, S. Reddy, S. Kotha, Spatial data mining using cluster analysis. Int. J. Comput. Sci. Inf. Technol. (2012)
M. Van der Laan, K. Pollard, J. Bryan, A new partitioning around medoids algorithm. J. Stat. Comput. Simul. 73(8), 575–584 (2003). https://doi.org/10.1080/0094965031000136012
G. Sheikholeslami, S. Chatterjee, A. Zhang, WaveCluster: a multi-resolution clustering approach for very large spatial databases, in Proceedings of the 24rd International Conference on Very Large Databases (1998)
R.T. Ng, J. Han, CLARANS: a method for clustering objects for spatial data mining. IEEE Trans. Knowl. Data Eng. 14(5), 1003–1016 (2002). https://doi.org/10.1109/TKDE.2002.1033770
M. Perumal, B. Velumani, A. Sadhasivam, K. Ramaswamy, Spatial data mining approaches for GIS—a brief review, in Emerging ICT for Bridging the Future—Proceedings of the 49th Annual Convention of the Computer Society of India CSI, vol. 2, pp. 579–592, 2015
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Robles Cárdenas, J.A., Pérez Torres, G. (2021). Geospatial Data Mining Techniques Survey. In: Oliva, D., Houssein, E.H., Hinojosa, S. (eds) Metaheuristics in Machine Learning: Theory and Applications. Studies in Computational Intelligence, vol 967. Springer, Cham. https://doi.org/10.1007/978-3-030-70542-8_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-70542-8_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-70541-1
Online ISBN: 978-3-030-70542-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)