A Two-Stage Unsupervised Dimension Reduction Method for Text Clustering

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 202)

Abstract

Feature selection is widely used in text clustering to reduce dimensions in the feature space. In this paper, we study and propose two-stage unsupervised feature selection methods to determine a subset of relevant features to improve accuracy of the underlying algorithm. We experiment with hybrid approach of feature selection—feature selection (FS–FS) and feature selection—feature extraction (FS–FE) methods. Initially, each feature in the document is scored on the basis of its importance for the clustering using two different feature selection methods individually Mean-Median (MM) and Mean Absolute Difference (MAD).In the second stage, in two different experiments, we hybridize them with a feature selection method absolute cosine (AC) and a feature extraction method principal component analysis (PCA) to further reduce the dimensions in the feature space. We perform comprehensive experiments to compare FS, FS–FS and FS–FE using k-mean clustering on Reuters-21578 dataset. The experimental results show that the two-stage feature selection methods are more effective to obtain good quality results by the underlying clustering algorithm. Additionally, we observe that FS–FE approach is superior to FS–FS approach.

Keywords

Feature selection Feature extraction Relevant Redundant Text clustering 

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References

  1. Salton, G.: Wong, A.: Yang, C.S.: A Vector Space Model for Automatic Indexing. Communications of the ACM 18(11), 613–620 (1975).Google Scholar
  2. Quinlan, J.R.: Induction of decision tree. Machine learning 1(1), 81-106 (1986).Google Scholar
  3. Maldonado, S.: Weber, R.: A wrapper method for feature selection using Support Vector Machines. Information Sciences179(13), 2208-2217 (2009).Google Scholar
  4. Church, K.W.: Hanks, P.: word association norm, mutual information and lexicography. In proceeding of ACL 27, 76-83, Vancouver, Canada (1989).Google Scholar
  5. Li, Y.: Luo, C.: Chung, S.M.: Text Clustering with Feature Selection by Using Statistical Data. IEEE Transactions On Knowledge And Data Engineering, 20(5), 641-652 (2008).Google Scholar
  6. Liu, L.: Kang, J.: Yu, J.: Wang, Z.: A comparative study on unsupervised feature selection methods for text clustering. In: IEEE International Conference on Natural Language Processing and Knowledge Engineering 597–601 (2005).Google Scholar
  7. Yang, Y.: Noise reduction in a statistical approach to text categorization. In proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval 256–263 (1995).Google Scholar
  8. Ferreira, A.: Figueiredo, M.: Unsupervised Feature Selection for Sparse Data. In proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 339-344 (2011).Google Scholar
  9. Ferreira, A.J.: Figueired, M.A.T.: Efficient Feature Selection Filters for High-Dimensional Data. Pattern Recognition Letters 33(13), 1794-1804 (2012).Google Scholar
  10. Pearson, K..On Lines and Planes of Closest filt to Systems of Points in Space. Philosophical Magazine 1(6), 559-572 (1901).Google Scholar
  11. Deerwester, S.: Improving Information Retrieval with Latent Semantic Indexing. In proceedings of the 51st Annual Meeting of the American Society for Information Science 25, 36–40 (1988).Google Scholar
  12. Hyvärinen, A.: Oja, E.: Independent component analysis: a tutorial. In Helsinki University of Technology, Laboratory of computer and Information Science (1999).Google Scholar
  13. Uguz, H.: A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm. Knowledge-Based Systems 24(7), 1024-1032 (2011).Google Scholar
  14. Uguz,H.:A hybrid system based on information gain and principal component analysis for the classification of transcranial Doppler signals. Computer Methods and Programs in Biomedicine 107(3), 598-609, 2012.Google Scholar
  15. Meng, J.: Lin, H.: Yu, Y.: A two-stage feature selection method for text categorization. Knowledge-Based Systems 62(7), 2793-2800 (2011).Google Scholar
  16. Song, W.: Park, S.C.: Genetic algorithm for text clustering based on latent semantic indexing. Computers and Mathematics with Applications 57(11-12), 1901-1907 (2009).Google Scholar
  17. Hsu, H.H.: Hsieh, C.W.: Lu, M.D.: Hybrid feature selection by combining filters and wrappers. Expert Systems with Applications 38(7), 8144–8150 (2011).Google Scholar
  18. Akadi, A.E.: Amine, A.: Ouardighi, A.E.: Aboutajdine, D.: A two-stage gene selection scheme utilizing MRMR filter and GA wrapper. KnowlInfSyst26(3), 487–500 (2011).Google Scholar
  19. MacQueen, J. B.: Some Methods for classification and Analysis of Multivariate Observations”. 1. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press. 281–297 (1967).Google Scholar
  20. Zhang, Y.: Ding, C.: Li, T.: Gene selection algorithm by combining reliefF and mRMR. IEEE 7th International Conference on Bioinformatics and Bioengineering. 1-10 (2008).Google Scholar
  21. Valle, S.: Li, W.: Qin, S.J.: Selection of the number of principal components: the variance of the reconstruction error criterion with a comparison to other methods. Ind, Engineering Chemistry Research 38(11), 4389–4401 (1999).Google Scholar
  22. Jilliffe, T.: Principal component analysis. ACM Computing Survey, Springer, Verlag, 1-47 (1986).Google Scholar
  23. Singh, P.K.: Machavolu, M.: Bharti, K.: Suda, R.: Analysis of Text Cluster Visualization in Emergent Self Organizing Maps Using Unigrams and Its Variations after Introducing Bigrams. In proce. of international conference on soft computing for problem solving, 967-978 (2011).Google Scholar
  24. Ferr, L.: Selection of components in principal component analysis: a comparison of methods, Computing and Statistical Data Analysis 19(6), 669–682 (1995).Google Scholar
  25. Unler, A.: Murat, A.: Chinnam, R.B.: mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification. Information Sciences 181(20), 4625–4641 (2011).Google Scholar
  26. Kira, K.: Rendell, L.: The feature selection problem: Traditional methods and a new algorithm. In: Association for the Advancement of Artificial Intelligence. AAAI Press and MIT Press, Cambridge, MA, USA. 129–134 (1992).Google Scholar
  27. Kononenko, I.: Estimating attributes: Analysis and extensions of RELIEF. In: Proc. of the European Conference on Machine Learning. Springer, Verlag, 171–182 (1994).Google Scholar
  28. Foithong, S.: Pinngern, O.: Attachoo, B.: Feature subset selection wrapper based on mutual information and rough sets. Expert Systems with Applications 39(1), 574-584, (2012).Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Computational Intelligence and DataMining Research LabABV-Indian Institute of Information Technology and Management GwaliorGwaliorIndia

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