An Unsupervised, Fast Correlation-Based Filter for Feature Selection for Data Clustering

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 285)

Abstract

Feature selection is an important method to provide both efficiency and effectiveness for high-dimension data clustering. However, most feature selection methods require prior knowledge such as class-label information to train the clustering module, where its performance depends on training data and types of learning machine. This paper presents a feature selection algorithm that does not require supervised feature assessment. We analyze relevance and redundancy among features and effectiveness to each target class to build a correlation-based filter. Compared to feature sets selected by existing methods, the experimental results show that performance of a feature set selected by the proposed method is comparably equal and better when it is tested on the RCV1v2 corpus and Isolet data set, respectively. However, our technique is simpler and faster and it is independent to types of learning machine.

Keywords

Feature selection Unsupervised learning Clustering Filter-based method Correlation Similarity Redundancy 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Almeida, L.P., Vasconcelos, A.R., Maia, M.G.: A Simple and Fast Term Selection Procedure for Text Clustering. In: Nedjah, N., Macedo Mourelle, L., Kacprzyk, J., França, F.G., De Souza, A. (eds.) Intelligent Text Categorization and Clustering, vol. 164, pp. 47-64. Springer Berlin Heidelberg (2009)Google Scholar
  2. 2.
    Alelyani, S., Tang, J., Liu, H.: Feature Selection for Clustering: A Review. In: Aggarwal, C., Reddy, C. (eds.) Data Clustering: Algorithms and Applications. CRC Press (2013)Google Scholar
  3. 3.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34, 1-47 (2002)Google Scholar
  4. 4.
    Ferreira, A.J., Figueiredo, M.A.T.: An unsupervised approach to feature discretization and selection. Pattern Recognition 45, 3048-3060 (2012)Google Scholar
  5. 5.
    Shamsinejadbabki, P., Saraee, M.: A new unsupervised feature selection method for text clustering based on genetic algorithms. J Intell Inf Syst 38, 669-684 (2012)Google Scholar
  6. 6.
    Luying, L., Jianchu, K., Jing, Y., Zhongliang, W.: A comparative study on unsupervised feature selection methods for text clustering. In: Natural Language Processing and Knowledge Engineering, 2005. IEEE NLP-KE ‘05. Proceedings of 2005 IEEE International Conference on, pp. 597-601. (Year)Google Scholar
  7. 7.
    Ferreira, A.J., Figueiredo, M.A.T.: Efficient feature selection filters for high-dimensional data. Pattern Recognition Letters 33, 1794-1804 (2012)Google Scholar
  8. 8.
    Ferreira, A., Figueiredo, M.: Efficient unsupervised feature selection for sparse data. In: EUROCON - International Conference on Computer as a Tool (EUROCON), 2011 IEEE, pp. 1-4. (Year)Google Scholar
  9. 9.
    Yanjun, L., Congnan, L., Chung, S.M.: Text Clustering with Feature Selection by Using Statistical Data. Knowledge and Data Engineering, IEEE Transactions on 20, 641-652 (2008)Google Scholar
  10. 10.
    Mitra, S., Kundu, P.P., Pedrycz, W.: Feature selection using structural similarity. Information Sciences 198, 48-61 (2012)Google Scholar
  11. 11.
    Guyon, I., Andr, #233, Elisseeff: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157-1182 (2003)Google Scholar
  12. 12.
    Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering 17, 491-502 (2005)Google Scholar
  13. 13.
    Somol, P., Novovicova, J., Pudil, P.: Efficient Feature Subset Selection and Subset Size Optimization. Pattern Recognition Recent Advances 75-97 (2010)Google Scholar
  14. 14.
    Yu, L., Liu, H.: Efficient Feature Selection via Analysis of Relevance and Redundancy. J. Mach. Learn. Res. 5, 1205-1224 (2004)Google Scholar
  15. 15.
    Liu, T., Liu, S., Chen, Z.: An Evaluation on Feature Selection for Text Clustering. In: In ICML, pp. 488-495. (Year)Google Scholar
  16. 16.
    Yang, Y., Pedersen, J.O.: A Comparative Study on Feature Selection in Text Categorization. In: 14th International Conference on Machine Learning, pp. 412-420. Morgan Kaufmann Publishers Inc., 657137 (Year)Google Scholar
  17. 17.
    Zonghu, W., Zhijing, L., Donghui, C., Kai, T.: A new partitioning based algorithm for document clustering. In: Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on, pp. 1741-1745. (Year)Google Scholar
  18. 18.
    Lewis, D.D., Yang, Y., Rose, T., Li, F.: RCV1: A New Benchmark Collection for Text Categorization Research. Journal of Machine Learning Research 5, 361-397 (2004)Google Scholar
  19. 19.
    Bache, K., Lichman, M.: UCI Machine Learning Repository. University of California, Irvine, School of Information and Computer Sciences, Irvine, CA (2013)Google Scholar
  20. 20.
    Mitra, P., Murthy, C.A., Pal, S.K.: Unsupervised feature selection using feature similarity. Pattern Analysis and Machine Intelligence, IEEE Transactions on 24, 301-312 (2002)Google Scholar
  21. 21.
    Shamsinejadbabki, P., Saraee, M.: A new unsupervised feature selection method for text clustering based on genetic algorithms. J Intell Inf Syst 1-16 (2011)Google Scholar
  22. 22.
    Achtert, E., Goldhofer, S., Kriegel, H.-P., Schubert, E., Zimek, A.: Evaluation of Clusterings - Metrics and Visual Support. In: ICDE’12, pp. 1285-1288. (2012)Google Scholar
  23. 23.
    Ruiz, R., Riquelme, J., Aguilar-Ruiz, J.: Heuristic Search over a Ranking for Feature Selection. In: Cabestany, J., Prieto, A., Sandoval, F. (eds.) Computational Intelligence and Bioinspired Systems, vol. 3512, pp. 498-503. Springer Berlin/Heidelberg (2005)Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  1. 1.Department of Computer Engineering, Faculty of EngineeringKasetsart UniversityBangkokThailand

Personalised recommendations