Rough-Set Based Hotspot Detection in Spatial Data

  • Mohd Shamsh TabarejEmail author
  • Sonajharia Minz
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1046)


A special type of cluster is called hotspots in the sense that objects in the hotspot are more active as compared to all others (appearance, density, etc.). The object in a general cluster has a similarity which is less than the object in the hotspot. In spatial data mining hotspots detection is a process of identifying the region where events are more likely to happen than the others. Hotspot analysis is mainly used in the analysis of health and crime data. In this paper, the health care data set is used to find the Hotspot of the health condition in India. The clustering algorithm is used to find the hotspot. Two clustering algorithm K-medoid and Rough K-medoid are implemented to find the cluster. K-medoid is used to find the spatial cluster, Rough K-medoid finds the cluster by removing boundary points and in this way find cluster which is denser. Granules are created on the clusters created using K-medoid and Rough K-medoid and point lying in each granule is counted. Granule containing points above a particular threshold is considered as a potential hotspot. To find the footprint of the hotspot convex hull is created on each detected hotspot. Also in this paper hotspot and footprint is defined mathematically.


Clustering K-medoid Rough-set Rough K-medoid Granules Convex hull Footprint Hotspot 


  1. 1.
    Aranganayagi, S., Thangavel, K.: Clustering categorical data using silhouette coefficient as a relocating measure. In: International Conference on Computational Intelligence and Multimedia Applications, vol. 2, pp. 13–17. IEEE (2007)Google Scholar
  2. 2.
    Bargiela, A., Pedrycz, W.: Granular computing. In: Handbook on Computational Intelligence: Volume 1: Fuzzy Logic, Systems, Artificial Neural Networks, and Learning Systems, pp. 43–66. World Scientific (2016)Google Scholar
  3. 3.
    Boldt, M., Borg, A.: A statistical method for detecting significant temporal hotspots using LISA statistics. In: 2017 European Intelligence and Security Informatics Conference (EISIC), pp. 123–126. IEEE (2017)Google Scholar
  4. 4.
    Di Martino, F., Pedrycz, W., Sessa, S.: Spatiotemporal extended fuzzy c-means clustering algorithm for hotspots detection and prediction. Fuzzy Sets Syst. 340, 109–126 (2018)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Dong, Y., Qian, S., Zhang, K., Zhai, Y.: A novel passenger hotspots searching algorithm for taxis in urban area. In: 2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), pp. 175–180. IEEE (2017)Google Scholar
  6. 6.
    Eftelioglu, E., Tang, X., Shekhar, S.: Geographically robust hotspot detection: a summary of results. In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pp. 1447–1456. IEEE (2015)Google Scholar
  7. 7.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226–231 (1996)Google Scholar
  8. 8.
    Grubesic, T.H.: On the application of fuzzy clustering for crime hot spot detection. J. Quant. Criminol. 22(1), 77 (2006)CrossRefGoogle Scholar
  9. 9.
    Harries, K.: Mapping crime and geographic information systems (1999).
  10. 10.
    Ishioka, F., Kawahara, J., Mizuta, M., Minato, S.I., Kurihara, K.: Evaluation of hotspot cluster detection using spatial scan statistic based on exact counting. Japan. J. Stat. Data Sci. 2, 1–22 (2019)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Jamil, M.S., Akbar, S.: Taxi passenger hotspot prediction using automatic ARIMA model. In: 2017 3rd International Conference on Science in Information Technology (ICSITech), pp. 23–28. IEEE (2017)Google Scholar
  12. 12.
    Lee, J., Jang, K.: Proactive detection of crash hotspots using in-vehicle driving recorder. In: 2016 3rd Asia- Pacific World Congress on Computer Science and Engineering (APWC on CSE), pp. 193–198. IEEE (2016)Google Scholar
  13. 13.
    Meiriza, A., Malik, R.F., Nurmaini, S., et al.: Spatio-temporal analysis of south sumatera hotspot distribution. In: 2017 International Conference on Electrical Engineering and Computer Science (ICECOS), pp. 198–201. IEEE (2017)Google Scholar
  14. 14.
    Pakhira, M.K., Bandyopadhyay, S., Maulik, U.: Validity index for crisp and fuzzy clusters. Pattern Recogn. 37(3), 487–501 (2004)CrossRefGoogle Scholar
  15. 15.
    Patel, A., Singh, P., et al.: New approach for k-mean and k-medoids algorithm. Int. J. Comput. Appl. Technol. Res. 2(1), 1–5 (2013)Google Scholar
  16. 16.
    Pawlak, Z.: Rough sets. Int. J. Comput. Inform. Sci. 11(5), 341–356 (1982)CrossRefGoogle Scholar
  17. 17.
    Perrot, A., Bourqui, R., Hanusse, N., Lalanne, F., Auber, D.: Large interactive visualization of density functions on big data infrastructure. In: 2015 IEEE 5th Symposium on Large Data Analysis and Visualization (LDAV), pp. 99–106. IEEE (2015)Google Scholar
  18. 18.
    Peters, G., Lampart, M., Weber, R.: Evolutionary rough k-medoid clustering. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets VIII. LNCS, vol. 5084, pp. 289–306. Springer, Heidelberg (2008). Scholar
  19. 19.
    Sheikh, Y.A., Khan, E.A., Kanade, T.: Mode-seeking by medoidshifts. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–8. IEEE (2007)Google Scholar
  20. 20.
    Verma, N., Baliyan, N.: Pam clustering based taxi hotspot detection for informed driving. In: 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT). pp. 1–7. IEEE (2017)Google Scholar
  21. 21.
    Wikipedia: Convex hull (2018).

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Computer and Systems SciencesJawaharlal Nehru UniversityNew DelhiIndia

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