Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Intelligent Systems

Editor-in-Chief: Fleyeh, Hasan

CiteScore 2018: 1.03

SCImago Journal Rank (SJR) 2018: 0.188
Source Normalized Impact per Paper (SNIP) 2018: 0.533

See all formats and pricing
More options …
Volume 26, Issue 3


Mining Spatial Association Rules to Automatic Grouping of Spatial Data Objects Using Multiple Kernel-Based Probabilistic Clustering

Y. Jayababu / G.P.S. Varma / A. Govardhan
  • University College of Engineering, Jawaharlal Nehru Technological University, Hyderabad, India
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2016-08-06 | DOI: https://doi.org/10.1515/jisys-2016-0044


With the extensive application of spatial databases to various fields ranging from remote sensing to geographical information systems, computer cartography, environmental assessment, and planning, discovery of interesting and hidden knowledge in the spatial databases is a considerable chore for classifying and using the spatial data and knowledge bases. The literature presents different spatial data mining methods to mine knowledge from spatial databases. In this paper, spatial association rules are mined to automatic grouping of spatial data objects using a candidate generation process with three constraint measures, such as support, confidence, and lift. Then, the proposed multiple kernel-based probabilistic clustering is applied to the associate vector to further group the spatial data objects. Here, membership probability based on probabilistic distance is used with multiple kernels, where exponential and tangential kernel functions are utilized. The performance of the proposed method is analyzed with three data sets of three different geometry types using the number of rules and clustering accuracy. From the experimentation, the results proved that the proposed multi-kernel probabilistic clustering algorithm achieved better accuracy as compared with the existing probabilistic clustering.

Keywords: Spatial database; association rule mining; clustering; probabilistic clustering; kernel


  • [1]

    R. Agrawal, T. Imielinski and A. Swami, Mining association rules between sets of items in large databases, in: Proceedings of the International Conference on Management of Data, ACM SIGMOD, pp. 207–216, Washington, DC, May 1993.Google Scholar

  • [2]

    A. Ben-Israel and C. Iyigun, Probabilistic D-clustering, J. Classif. 25 (2008), 5–26.CrossrefGoogle Scholar

  • [3]

    E. Clementini, P. Di Felice and K. Koperski, Mining multiple-level spatial association rules for objects with a broad boundary, Data Knowl. Eng. 34 (2000), 251–270.CrossrefGoogle Scholar

  • [4]

    T. H. D. Dao and J. C. Thill, A comprehensive framework for spatial association rule mining, in: The 7th International Conference on Geographic Information Science, Columbus, OH, USA, September 18–21, 2012.Google Scholar

  • [5]

    Q. Ding, Q. Ding and W. Perrizo, PARM – an efficient algorithm to mine association rules from spatial data, IEEE Trans. Sys. Man Cybern B Cybern. 38 (2008), 1513–1524.Google Scholar

  • [6]

    B. Fan, A hybrid spatial data clustering method for site selection: the data driven approach of GIS mining, Expert Syst. Appl. 36 (2009), 3923–3936.Web of ScienceCrossrefGoogle Scholar

  • [7]

    W. J. Frawley, G. Piatetsky-Shapiro and C. J. Matheus, Knowledge discovery in databases: an overview, in: Knowledge Discovery in Databases, G. Piatetsky-Shapiro and W. J. Frawley, eds., pp. 1–27, AAAI/MIT Press, Cambridge, MA, 1991.Google Scholar

  • [8]

    Y. Guo, J. Gao and L. Feng, Random spatial subspace clustering, Knowl.-Based Syst. 74 (2015), 106–118.Google Scholar

  • [9]

    U. Gupta and N. Ranganathan, A game theoretic approach for simultaneous compaction and equipartitioning of spatial data sets, IEEE Trans. Knowl. Data Eng. 22 (2010), 465–478.Web of ScienceCrossrefGoogle Scholar

  • [10]

    Z. Jiang, S. Shekhar, X. Zhou, J. Knight and J. Corcoran, Focal-test-based spatial decision tree learning, IEEE Trans. Knowl. Data Eng. 27 (2015), 1547–1559.CrossrefGoogle Scholar

  • [11]

    K. Koperski and J. Han, Discovery of spatial association rules in geographic information databases, Adv. Spat. Databases 951 (1995), 47–66.Google Scholar

  • [12]

    P. Laube, M. Berg and M. van Kreveld, Spatial support and spatial confidence for spatial association rules, in: Headway in Spatial Data Handling, Lecture Notes in Geoinformation and Cartography, pp. 575–593, 2008.Google Scholar

  • [13]

    Q. Liu, M. Deng, Y. Shi and J. Wang, A density-based spatial clustering algorithm considering both spatial proximity and attribute similarity, Comput. Geosci. 46 (2012), 296–309.Web of ScienceCrossrefGoogle Scholar

  • [14]

    J. B. MacQueen, Some methods for classification and analysis of multivariate observations, in: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297, University of California Press, Berkeley, CA, 1963.Google Scholar

  • [15]

    A. Mukhopadhyay, U. Maulik, S. Bandyopadhyay and C. A. Coello Coello, A survey of multiobjective evolutionary algorithms for data mining: part I, IEEE Trans. Evolut. Comput. 18 (2014), 4–19.CrossrefGoogle Scholar

  • [16]

    E. Packer, P. Bak, M. Nikkila, V. Polishchuk and H. J. Ship, Visual analytics for spatial clustering: using a heuristic approach for guided exploration, IEEE Trans. Vis. Comput. Graph. 19 (2013), 2179–2188.Web of ScienceCrossrefGoogle Scholar

  • [17]

    A. H. Pilevar and M. Sukumar, GCHL: a grid-clustering algorithm for high-dimensional very large spatial data bases, Pattern Recognit. Lett. 26 (2005), 999–1010.CrossrefGoogle Scholar

  • [18]

    C. R. Shyu, M. Klaric, G. Scott and W. K. Mahamaneerat, Knowledge discovery by mining association rules and temporal-spatial information from large-scale geospatial image databases, in: Proceedings of IEEE International Conference on Geoscience and Remote Sensing Symposium, pp. 17–20, 2006.Google Scholar

  • [19]

    W. R. Tobler, A computer model simulation of urban growth in the Detroit region, Econ. Geogr. 46 (1970), 234–240.CrossrefGoogle Scholar

  • [20]

    X. Wu, X. Zhu, G. Q. Wu and W. Ding, Data mining with big data, IEEE Trans. Knowl. Data Eng. 26 (2014), 97–107.CrossrefGoogle Scholar

  • [21]

    K. R. Zalik and B. Zalik, A sweep-line algorithm for spatial clustering, Adv. Eng. Softw. 40 (2009), 445–451.Web of ScienceGoogle Scholar

  • [22]

    Q. Zhao, Y. Shi, Q. Liu and P. Franti, A grid-growing clustering algorithm for geo-spatial data, Pattern Recognit. Lett. 53 (2015), 77–84.CrossrefGoogle Scholar

About the article

Received: 2016-04-16

Published Online: 2016-08-06

Published in Print: 2017-07-26

Citation Information: Journal of Intelligent Systems, Volume 26, Issue 3, Pages 561–572, ISSN (Online) 2191-026X, ISSN (Print) 0334-1860, DOI: https://doi.org/10.1515/jisys-2016-0044.

Export Citation

©2017 Walter de Gruyter GmbH, Berlin/Boston.Get Permission

Comments (0)

Please log in or register to comment.
Log in