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.
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References
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufman Publishers, Oxford (2006)
Keim, D.A.: Visual exploration of large data sets. Commun. ACM 44, 38–44 (2001)
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)
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)
Abello, J., Korn, J.: MGV: a system for visualizing massive multidigraphs. IEEE Transactions on Visualization and Computer Graphics 8, 21–38 (2002)
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)
Dash, M., Liu, H.: Consistency-based search in feature selection. Artificial Intelligence 151, 155–176 (2003)
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)
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)
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)
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)
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)
Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)
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)
Hedar, A.-R., Wang, J., Fukushima, M.: Tabu search for attribute reduction in rough set theory. Soft. Comput. 12, 909–918 (2008)
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)
Jensen, R., Shen, Q.: Finding Rough Set Reducts with Ant Colony Optimization. In: Workshop, U.K. (ed.) UK Workshop on Computational Intelligence, UK (2003)
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)
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)
Jaddi, N.S., Abdullah, S.: Nonlinear Great Deluge Algorithm for Rough Set Attribute Reduction. Journal of Information Science & Engineering 29, 49–62 (2013)
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)
Gunter, D.: New optimization heuristics (The Great Deluge Algorithm and Record to Record Travel). Computational Physic, 86–92 (1993)
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)
Burke, E., Bykov, Y., Newall, J., Petrovic, S.: A time-predefined local search approach to exam timetabling problems. IIE Transactions 36, 509–528 (2004)
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)
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)
Migut, M., Worring, M.: Visual exploration of classification models for various data types in risk assessment. Information Visualization (2012)
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)
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Jaddi, N.S., Abdullah, S. (2013). An Interactive Rough Set Attribute Reduction Using Great Deluge Algorithm. In: Zaman, H.B., Robinson, P., Olivier, P., Shih, T.K., Velastin, S. (eds) Advances in Visual Informatics. IVIC 2013. Lecture Notes in Computer Science, vol 8237. Springer, Cham. https://doi.org/10.1007/978-3-319-02958-0_27
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DOI: https://doi.org/10.1007/978-3-319-02958-0_27
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