Foundations of Rough Biclustering

  • Marcin Michalak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7268)

Abstract

Amongst the algorithms for biclustering using some rough sets based steps none of them uses the formal concept of rough bicluster with its lower and upper approximation. In this short article the new foundations of rough biclustering are described. The new relation β generates β −description classes that build the rough bicluster defined with its lower and upper approximation.

Keywords

rough sets biclustering upper and lower approximation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proc. of the 8th Int. Conf. on Intell. Syst. for Mol. Biol., pp. 93–103 (2000)Google Scholar
  2. 2.
    Emilyn, J.J., Ramar, K.: Rough Set Based Clustering of Gene Expression Data: A Survey. Int. J. of Eng. Sci. and Technol. 2(12), 7160–7164 (2010)Google Scholar
  3. 3.
    Emilyn, J.J., Ramar, K.: A Rough Set Based Gene Expression Clustering Algorithm. J. of Comput. Sci. 7(7), 986–990 (2011)CrossRefGoogle Scholar
  4. 4.
    Emilyn, J.J., Ramar, K.: A Rough Set Based Novel Biclustering Algorithm for Gene Expression Data. In: Int. Conf. on Electron. Comput. Techn., pp. 284–288 (2011)Google Scholar
  5. 5.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proc. of 2nd Int. Conf. on Knowl. Discov. and Data Min., pp. 226–231 (1996)Google Scholar
  6. 6.
    Hartigan, J.A.: Direct Clustering of a Data Matrix. J. Am. Stat. Assoc. 67(337), 123–129 (1972)Google Scholar
  7. 7.
    MacQueen, J.: Some Methods for Classification and Analysis of Multivariate Observations. In: Proc. Fifth Berkeley Symp. on Math. Statist. and Prob., pp. 281–297 (1967)Google Scholar
  8. 8.
    Michalak, M., Stawarz, M.: Generating and Postprocessing of Biclusters from Discrete Value Matrices. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds.) ICCCI 2011, Part I. LNCS, vol. 6922, pp. 103–112. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Pawlak, Z.: Rough Sets. J. of Comput. and Inf. Sci. 5(11), 341–356 (1982)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishing (1991)Google Scholar
  11. 11.
    Pensa, R., Boulicaut, J.F.: Constrained Co-clustering of Gene Expression Data. In: Proc. SIAM Int. Conf. on Data Min., SDM 2008, pp. 25–36 (2008)Google Scholar
  12. 12.
    Wang, R., Miao, D., Li, G., Zhang, H.: Rough Overlapping Biclustering of Gene Expression Data. In: Proc. of the 7th IEEE Int. Conf. on Bioinforma. and Bioeng. (2007)Google Scholar
  13. 13.
    Yang, E., Foteinou, P.T., King, K.R., Yarmush, M.L., Androulakis, I.P.: A Novel Non-overlapping biclustering Algorithm for Network Generation Using Living Cell Array Data. Bioinforma. 17(23), 2306–2313 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marcin Michalak
    • 1
  1. 1.Silesian University of TechnologyGliwicePoland

Personalised recommendations