A Rough Clustering Algorithm for Mining Outliers in Categorical Data

  • N. N. R. Ranga Suri
  • Musti Narasimha Murty
  • Gopalasamy Athithan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)


Outlier detection is an important data mining task with applications in various domains. Mining of outliers in data has to deal with uncertainty regarding the membership of such outlier objects to one of the normal groups (classes) of objects. In this context, a soft computing approach based on rough sets happens to be a better choice to handle such mining tasks. Motivated by this requirement, a novel rough clustering algorithm is proposed here by modifying the basic k-modes algorithm to incorporate the lower and upper approximation properties of rough sets. The proposed algorithm includes the necessary computational steps required for determining the object assignment to various clusters and the modified centroid (mode) computation on categorical data. An experimental evaluation of the proposed rough k-modes algorithm is also presented here to demonstrate its performance in detecting outliers using various benchmark categorical data sets.


Data mining Soft computing Rough sets Outlier detection Data Clustering 


  1. 1.
    Albanese, A., Pal, S.K., Petrosino, A.: Rough sets, kernel set and spatio-temporal outlier detection. IEEE Trans. on Knowledge and Data Engineering (2012) (online)Google Scholar
  2. 2.
    Asuncion, A., Newman, D.J.: UCI machine learning repository (2007),
  3. 3.
    Cao, F., Liang, J., Bai, L.: A new initialization method for categorical data clustering. Expert Systems with Applications 36, 10223–10228 (2009)CrossRefGoogle Scholar
  4. 4.
    Huang, Z.: A fast clustering algorithm to cluster very large categorical data sets in data mining. In: SIGMOD DMKD Workshop, pp. 1–8 (1997)Google Scholar
  5. 5.
    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
  6. 6.
    Ng, M.K., Li, M.J., Huang, J.Z., He, Z.: On the impact of dissimilarity measure in k-modes clustering algorithm. IEEE PAMI 29(3), 503–507 (2007)CrossRefGoogle Scholar
  7. 7.
    Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Peters, G.: Some refinements of rough k-means clustering. Pattern Recognition 39, 1481–1491 (2006)CrossRefzbMATHGoogle Scholar
  9. 9.
    Suri, N.N.R.R., Murty, M.N., Athithan, G.: Data mining techniques for outlier detection. In: Visual Analytics and Interactive Technologies: Data, Text and Web Mining Applications, ch. 2, pp. 22–38. IGI Global, New York (2011)Google Scholar
  10. 10.
    Suri, N.N.R.R., Murty, M.N., Athithan, G.: An algorithm for mining outliers in categorical data through ranking. In: IEEE HIS, Pune, India, pp. 247–252 (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • N. N. R. Ranga Suri
    • 1
  • Musti Narasimha Murty
    • 2
  • Gopalasamy Athithan
    • 1
    • 3
  1. 1.Centre for AI and Robotics (CAIR)BangaloreIndia
  2. 2.Dept of CSAIndian Institute of Science (IISc)BangaloreIndia
  3. 3.Presently Working at Scientific Analysis Group (SAG)DelhiIndia

Personalised recommendations