Outlier Detection Using Rough Sets

  • N. N. R. Ranga SuriEmail author
  • Narasimha Murty M
  • G. Athithan
Part of the Intelligent Systems Reference Library book series (ISRL, volume 155)


Clustering-based methods for outlier detection are preferred in many contemporary applications due to the abundance of methods available for data clustering. However, the uncertainty regarding the cluster membership of an outlier object needs to be handled appropriately during the clustering process. Addressing this issue, this chapter delves on soft computing methodologies based on rough sets for clustering data involving outliers. In specific, the case of data comprising categorical attributes is looked at in detail for carrying out outlier detection through clustering by employing rough sets. Experimental observations on benchmark data sets indicate that soft computing techniques indeed produce promising results for outlier detection over their counterparts.


  1. 1.
    Asharaf, S., Murty, M.N., Shevade, S.K.: Rough set based incremental clustering of interval data. Pattern Recogn. Lett. 27, 515–519 (2006)CrossRefGoogle Scholar
  2. 2.
    Dua, D., Efi, K.T.: UCI machine learning repository (2017). URL
  3. 3.
    Huang, Z.: A fast clustering algorithm to cluster very large categorical data sets in data mining. In: SIGMOD Data Mining and Knowledge Discovery Workshop, pp. 1–8 (1997)Google Scholar
  4. 4.
    Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31, 651–666 (2010)CrossRefGoogle Scholar
  5. 5.
    Jiang, F., Sui, Y., Cao, C.: Some issues about outlier detection in rough set theory. Expert Syst. Appl. 36, 4680–4687 (2009)CrossRefGoogle Scholar
  6. 6.
    Joshi, M., Lingras, P.: Enhancing rough clustering with outlier detection based on evidential clustering. In: RSFDGrC. LNCS, vol. 8170, pp. 127–137. Springer, Berlin (2013)Google Scholar
  7. 7.
    Kaiiali, M., Wankar, R., Rao, C.R., Agarwal, A.: A rough set based PCM for authorizing grid resources. In: 10th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 391–396. Cairo, Egypt (2010)Google Scholar
  8. 8.
    Li, M., Deng, S., Wang, L., Feng, S., Fan, J.: Heirarchical clustering algorithm for categorical data using a probabilistic rough set model. Knowl. Based Syst. 65, 60–71 (2014)CrossRefGoogle Scholar
  9. 9.
    Lingras, P.: Rough set clustering for web mining. In: IEEE FUZZ, pp. 1039–1044 (2002)Google Scholar
  10. 10.
    Lingras, P., Peters, G.: Applying rough set concepts to clustering. In: Rough Sets: Selected Methods and Applications in Management and Engineering, pp. 23–38. Springer, London (2012)CrossRefGoogle Scholar
  11. 11.
    Lingras, P., West, C.: Interval set clustering of web users with rough k-means. J. Intell. Inf. Syst. 23(1), 5–16 (2004)CrossRefGoogle Scholar
  12. 12.
    Maji, P., Pal, S.K.: Fuzzy-rough sets for information measures and selection of relevant genes from microarray data. IEEE Trans. Syst. Man Cybern. Part B 40(3), 741–752 (2010)CrossRefGoogle Scholar
  13. 13.
    Masson, M., Denoeux, T.: ECM: an evidential version of the fuzzy c-means algorithm. Pattern Recogn. 41, 1384–1397 (2008)CrossRefGoogle Scholar
  14. 14.
    Mi, H.: Discovering local outlier based on rough clustering. In: 3rd International Workshop on Intelligent Systems and Applications (ISA), pp. 1–4. IEEE (2011)Google Scholar
  15. 15.
    Nguyen, H.S., Pal, S.K., Skowron, A.: Rough sets and fuzzy sets in natural computing. Theor. Comput. Sci. 412(42), 5816–5819 (2011)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Pal, S.K., Ghosh, A.: Guest editorial: soft computing data mining. Inf. Sci. 163, 1–3 (2004)CrossRefGoogle Scholar
  17. 17.
    Parmar, D., Wu, T., Blackhurst, J.: Mmr: an algorithm for clustering categorical data using rough set theory. Data Knowl. Eng. 63, 879–893 (2007)CrossRefGoogle Scholar
  18. 18.
    Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)CrossRefGoogle Scholar
  19. 19.
    Peters, G.: Some refinements of rough k-means clustering. Pattern Recogn. 39, 1481–1491 (2006)CrossRefGoogle Scholar
  20. 20.
    Skowron, A., Jankowski, A., Swiniarski, R.W.: 30 years of rough sets and future perspectives. RSFDGrC. LNCS, vol. 8170, pp. 1–10. Springer, Halifax, Canada (2013)Google Scholar
  21. 21.
    Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowinski, R. (ed.) Intelligent Decision Support—Handbook of Applications and Advances of the Rough Sets Theory, pp. 331–362 (1992)CrossRefGoogle Scholar
  22. 22.
    Suri, N.N.R.R., Murty, M.N., Athithan, G.: A rough clustering algorithm for mining outliers in categorical data. In: Engel, P.M. (ed.) 5th International Conference on Pattern Recognition and Machine Intelligence (PReMI). LNCS, vol. 8251, pp. 170–175. Springer, Berlin (2013)Google Scholar
  23. 23.
    Suri, N.N.R.R., Murty, M.N., Athithan, G.: A ranking-based algorithm for detection of outliers in categorical data. Int. J. Hybrid Intell. Syst. (IJHIS) 11(1), 1–11 (2014)CrossRefGoogle Scholar
  24. 24.
    Suri, N.N.R.R., Murty, M.N., Athithan, G.: Detecting outliers in categorical data through rough clusteringa. Nat. Comput. 15, 385–394 (2016)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • N. N. R. Ranga Suri
    • 1
    Email author
  • Narasimha Murty M
    • 2
  • G. Athithan
    • 3
  1. 1.Centre for Artificial Intelligence and Robotics (CAIR)BangaloreIndia
  2. 2.Department of Computer Science and AutomationIndian Institute of Science (IISc)BangaloreIndia
  3. 3.Defence Research and Development Organization (DRDO)New DelhiIndia

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