Advertisement

Combining Rough Clustering Schemes as a Rough Ensemble

  • Pawan LingrasEmail author
  • Farhana Haider
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9436)

Abstract

One of the challenges of big data is to combine results of data mining obtained from a distributed dataset. The objective is to minimize the amount data transfer with minimum information loss. A generic combination process will not necessarily provide an optimal ensemble of results. In this paper, we describe a rough clustering problem that leads to a natural ordering of clusters. These ordered rough clusterings are then combined while preserving the properties of rough clustering. A time series dataset of commodity prices is clustered using two different representations to demonstrate the ordered rough clustering process. The information from the ordering of clusters is shown to help us retain salient aspects of individual rough clustering schemes.

Keywords

Clustering Ensemble Rough sets Granular computing Financial time series Volatility 

References

  1. 1.
    Black, F., Scholes, M.: The pricing of options and corporate liabilities. J. Polit. Econ. 81(3), 637–654 (1973)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Fern, X.Z., Lin, W.: Cluster ensemble selection. Stat. Anal. Data Mining 1(3), 128–141 (2008)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Fred, A.: Finding consistent clusters in data partitions. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, p. 309. Springer, Heidelberg (2001) CrossRefGoogle Scholar
  4. 4.
    Gao, C., Pedrycz, W., Miao, D.: Rough subspace-based clustering ensemble for categorical data. Soft Comput. 5(3), 1–16 (2013)zbMATHGoogle Scholar
  5. 5.
    Ghosh, J., Acharya, A.: Cluster ensembles. Wiley Interdisc. Rev. Data Mining Knowl. Discov. 1(4), 305–315 (2011)CrossRefGoogle Scholar
  6. 6.
    Gionis, A., Mannila, H., Tsaparas, P.: Clustering aggregation. ACM Trans. Knowl. Discov. Data 1(1), 4 (2007)CrossRefGoogle Scholar
  7. 7.
    Jia, Z., Han, J.: An improved model of executive stock option based on rough set and support vector machines. In: 2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA 2008, 1, pp. 256–261. IEEE (2008)Google Scholar
  8. 8.
    Joshi, M., Lingras, P.: Evolutionary and iterative crisp and rough clustering II: experiments. In: Chaudhury, S., Mitra, S., Murthy, C.A., Sastry, P.S., Pal, S.K. (eds.) PReMI 2009. LNCS, vol. 5909, pp. 621–627. Springer, Heidelberg (2009). http://dx.doi.org/10.1007/978-3-642-11164-8CrossRefGoogle Scholar
  9. 9.
    Lingras, P., Haider, F.: Rough ensemble clustering. In: Intelligent Data Analysis, Special Issue on Business Analytics in Finance and Industry (2014)Google Scholar
  10. 10.
    Lingras, P., West, C.: Interval set clustering of web users with rough k-means. J. Intell. Inf. Sys. 23(1), 5–16 (2004)CrossRefGoogle Scholar
  11. 11.
    Mitra, S.: An evolutionary rough partitive clustering. Pattern Recogn. Lett. 25(12), 1439–1449 (2004)CrossRefGoogle Scholar
  12. 12.
    Nair, B.B., Mohandas, V., Sakthivel, N.: A decision treerough set hybrid system for stock market trend prediction. Int. J. Comput. Appl. 6(9), 1–6 (2010)Google Scholar
  13. 13.
    Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11(5), 341–356 (1982)CrossRefGoogle Scholar
  14. 14.
    Pawlak, Z.: Fuzzy Logic for the Management of Uncertainty. Rough sets: a new approach to vagueness. Wiley, Newyork (1992) Google Scholar
  15. 15.
    Peters, G.: Some refinements of rough k-means clustering. Pattern Recogn. 39(8), 1481–1491 (2006)CrossRefGoogle Scholar
  16. 16.
    Peters, G., Crespo, F., Lingras, P., Weber, R.: Soft clustering-fuzzy and rough approaches and their extensions and derivatives. Int. J. Approximate Reasoning 54(2), 307–322 (2013)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Polkowski, L., Skowron, A.: Rough mereology: a new paradigm for approximate reasoning. Int. J. Approximate Reasoning 15(4), 333–365 (1996)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Skowron, A., Stepaniuk, J.: Information granules in distributed environment. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS (LNAI), vol. 1711, pp. 357–366. Springer, Heidelberg (1999) CrossRefGoogle Scholar
  19. 19.
    Strehl, A., Ghosh, J.: Cluster ensembles - a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2003)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Vega-Pons, S., Ruiz-Shulcloper, J.: A survey of clustering ensemble algorithms. Int. J. Pattern Recogn. Artif. Intell. 25(03), 337–372 (2011)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Yao, J., Herbert, J.P.: Financial time-series analysis with rough sets. Appl. Soft Comput. 9(3), 1000–1007 (2009)CrossRefGoogle Scholar
  22. 22.
    Yao, Y., Li, X., Lin, T., Liu, Q.: Representation and classification of rough set models. In: Proceeding of Third International Workshop on Rough Sets and Soft Computing. pp. 630–637 (1994)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  1. 1.Mathematics and Computing ScienceSaint Maryś UniversityHalifaxCanada

Personalised recommendations