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An Interactive Rough Set Attribute Reduction Using Great Deluge Algorithm

  • Najmeh Sadat Jaddi
  • Salwani Abdullah
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8237)

Abstract

Dimensionality reduction from an information system is a problem of eliminating unimportant attributes from the original set of attributes while avoiding loss of information in data mining process. In this process, a subset of attributes that is highly correlated with decision attributes is selected. In this paper, performance of the great deluge algorithm for rough set attribute reduction is investigated by comparing the method with other available approaches in the literature in terms of cardinality of obtained reducts (subsets), time required to obtain reducts, number of calculating dependency degree functions, number of rules generated by reducts, and the accuracy of the classification. An interactive interface is initially developed that user can easily select the parameters for reduction. This user interface is developed toward visual data mining.The carried out model has been tested on the standard datasets available in the UCI machine learning repository. Experimental results show the effectiveness of the method especially with relation to the time and accuracy of the classification using generated rules. The method outperformed other approaches in M-of-N, Exactly, and LED datasets with achieving 100% accuracy.

Keywords

interactive data mining great deluge algorithm rough set theory attribute reduction classification 

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References

  1. 1.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufman Publishers, Oxford (2006)Google Scholar
  2. 2.
    Keim, D.A.: Visual exploration of large data sets. Commun. ACM 44, 38–44 (2001)CrossRefGoogle Scholar
  3. 3.
    Havre, S., Hetzler, E., Whitney, P., Nowell, L.: ThemeRiver: visualizing thematic changes in large document collections. IEEE Transactions on Visualization and Computer Graphics 8, 9–20 (2002)CrossRefGoogle Scholar
  4. 4.
    Stolte, C., Tang, D., Hanrahan, P.: Polaris: A System for Query, Analysis, and Visualization of Multidimensional Relational Databases. IEEE Transactions on Visualization and Computer Graphics 8, 52–65 (2002)CrossRefGoogle Scholar
  5. 5.
    Abello, J., Korn, J.: MGV: a system for visualizing massive multidigraphs. IEEE Transactions on Visualization and Computer Graphics 8, 21–38 (2002)CrossRefGoogle Scholar
  6. 6.
    Zhen, L., Xiangshi, R., Chaohai, Z.: User interface design of interactive data mining in parallel environment. In: Proceedings of the 2005 International Conference on Active Media Technology, AMT 2005, pp. 359–363 (2005)Google Scholar
  7. 7.
    Dash, M., Liu, H.: Consistency-based search in feature selection. Artificial Intelligence 151, 155–176 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Lihe, G.: A New Algorithm for Attribute Reduction Based on Discernibility Matrix. In: Cao, B.-Y. (ed.) Fuzzy Information and Engineering. ASC, vol. 40, pp. 373–381. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Kudo, Y., Murai, T.: A Heuristic Algorithm for Attribute Reduction Based on Discernibility and Equivalence by Attributes. In: Torra, V., Narukawa, Y., Inuiguchi, M. (eds.) MDAI 2009. LNCS, vol. 5861, pp. 351–359. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Li, H., Zhang, W., Xu, P., Wang, H.: Rough Set Attribute Reduction in Decision Systems. In: Wang, G.-Y., Peters, J.F., Skowron, A., Yao, Y. (eds.) RSKT 2006. LNCS (LNAI), vol. 4062, pp. 135–140. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Liu, H., Li, J., Wong, L.: A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns. In: Lathrop, R., Nakai, K., Miyano, S., Takagi, T., Kanehisa, M. (eds.) Genome Informatics 2002, vol. 13, pp. 51–60. Universal Academy Press, Tokyo (2002)Google Scholar
  12. 12.
    Hu, Q.-H., Li, X., Yu, D.-R.: Analysis on Classification Performance of Rough Set Based Reducts. In: Yang, Q., Webb, G. (eds.) PRICAI 2006. LNCS (LNAI), vol. 4099, pp. 423–433. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Jensen, R., Qiang, S.: Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches. IEEE Transactions on Knowledge and Data Engineering 16, 1457–1471 (2004)CrossRefGoogle Scholar
  15. 15.
    Hedar, A.-R., Wang, J., Fukushima, M.: Tabu search for attribute reduction in rough set theory. Soft. Comput. 12, 909–918 (2008)CrossRefzbMATHGoogle Scholar
  16. 16.
    Jue, W., Hedar, A.R., Guihuan, Z., Shouyang, W.: Scatter Search for Rough Set Attribute Reduction. In: International Joint Conference on Computational Sciences and Optimization, CSO 2009, pp. 531–535 (2009)Google Scholar
  17. 17.
    Jensen, R., Shen, Q.: Finding Rough Set Reducts with Ant Colony Optimization. In: Workshop, U.K. (ed.) UK Workshop on Computational Intelligence, UK (2003)Google Scholar
  18. 18.
    Ke, L., Feng, Z., Ren, Z.: An efficient ant colony optimization approach to attribute reduction in rough set theory. Pattern Recogn. Lett. 29, 1351–1357 (2008)CrossRefGoogle Scholar
  19. 19.
    Abdullah, S., Jaddi, N.S.: Great Deluge Algorithm for Rough Set Attribute Reduction. In: Zhang, Y., Cuzzocrea, A., Ma, J., Chung, K.-i., Arslan, T., Song, X. (eds.) DTA/BSBT 2010. CCIS, vol. 118, pp. 189–197. Springer, Heidelberg (2010)Google Scholar
  20. 20.
    Jaddi, N.S., Abdullah, S.: Nonlinear Great Deluge Algorithm for Rough Set Attribute Reduction. Journal of Information Science & Engineering 29, 49–62 (2013)Google Scholar
  21. 21.
    Mafarja, M., Abdullah, S.: Modified great deluge for attribute reduction in rough set theory. In: 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 1464–1469 (2011)Google Scholar
  22. 22.
    Gunter, D.: New optimization heuristics (The Great Deluge Algorithm and Record to Record Travel). Computational Physic, 86–92 (1993)Google Scholar
  23. 23.
    Burke, E.K., Abdullah, S.: A Multi-start Large Neighbourhood Search Approach with Local Search Methods for Examination Timetabling. In: Long, D., Smith, S., Borrajo, D., McCluskey, L. (eds.) Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS 2006), Cumbria, UK (2006)Google Scholar
  24. 24.
    Burke, E., Bykov, Y., Newall, J., Petrovic, S.: A time-predefined local search approach to exam timetabling problems. IIE Transactions 36, 509–528 (2004)CrossRefGoogle Scholar
  25. 25.
    Landa-Silva, D., Obit, J.H.: Evolutionary Non-linear Great Deluge for University Course Timetabling. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds.) HAIS 2009. LNCS, vol. 5572, pp. 269–276. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  26. 26.
    McMullan, P.: An Extended Implementation of the Great Deluge Algorithm for Course Timetabling. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007, Part I. LNCS, vol. 4487, pp. 538–545. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  27. 27.
    Migut, M., Worring, M.: Visual exploration of classification models for various data types in risk assessment. Information Visualization (2012)Google Scholar
  28. 28.
    Stahl, F., Gabrys, B., Gaber, M.M., Berendsen, M.: An overview of interactive visual data mining techniques for knowledge discovery. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3, 239–256 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Najmeh Sadat Jaddi
    • 1
  • Salwani Abdullah
    • 1
  1. 1.Data Mining and Optimization Research Group (DMO), Center for Artificial Intelligence Technology, Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia

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