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Issue No.02 - Feb. (2013 vol.25)
pp: 274-284
Hongmei Chen , Southwest Jiaotong University, Chengdu
Tianrui Li , Southwest Jiaotong University, Chengdu
Jianhui Lin , Southwest Jiaotong University, Chengdu
Chengxiang Hu , Chuzhou University, Chuzhou
Approximations of a concept by a variable precision rough-set model (VPRS) usually vary under a dynamic information system environment. It is thus effective to carry out incremental updating approximations by utilizing previous data structures. This paper focuses on a new incremental method for updating approximations of VPRS while objects in the information system dynamically alter. It discusses properties of information granulation and approximations under the dynamic environment while objects in the universe evolve over time. The variation of an attribute's domain is also considered to perform incremental updating for approximations under VPRS. Finally, an extensive experimental evaluation validates the efficiency of the proposed method for dynamic maintenance of VPRS approximations.
Approximation methods, Information systems, Educational institutions, Approximation algorithms, Electronic mail, Rough sets, Computational modeling, incremental updating, Variable precision rough-set model, knowledge discovery, granular computing, information systems
Hongmei Chen, Tianrui Li, Da Ruan, Jianhui Lin, Chengxiang Hu, "A Rough-Set-Based Incremental Approach for Updating Approximations under Dynamic Maintenance Environments", IEEE Transactions on Knowledge & Data Engineering, vol.25, no. 2, pp. 274-284, Feb. 2013, doi:10.1109/TKDE.2011.220
[1] L.A. Zadeh, "Towards a Theory of Fuzzy Information Granulation and Its Centrality in Human Reasoning and Fuzzy Logic," Fuzzy Sets and Systems, vol. 90, no. 2, pp. 111-127, 1997.
[2] L.A. Zadeh, "Fuzzy Logic=Computing with Words," IEEE Trans. Fuzzy Systems, vol. 4, no. 1, pp. 103-111, Feb. 1996.
[3] J.T. Yao, "Information Granulation and Granular Relationships," Proc. IEEE Conf. Granular Computing (GrC), pp. 326-329, 2005.
[4] J.T. Yao and Y.Y. Yao, "A Granular Computing Approach to Machine Learning," Proc. First Int'l Conf. Fuzzy Systems and Knowledge Discovery, pp. 732-736, 2002.
[5] Y.Y. Yao and N. Zhong, "Potential Applications of Granular Computing in Knowledge Discovery and Data Mining," Proc. World Multiconf. Systemics, Cybernetics and Informatics, pp. 573-580, 1999.
[6] Y.Y. Yao, "Information Granulation and Rough Set Approximation," Int'l J. Intelligent Systems, vol. 16, no. 1, pp. 87-104, 2001.
[7] Y.Y. Yao, "Perspectives of Granular Computing," Proc. IEEE Int'l Conf. Granular Computing (GrC), pp. 85-90, 2005.
[8] Y.Y. Yao, "Interpreting Concept Learning in Cognitive Informatics and Granular Computing," IEEE Trans. Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 39, no. 4, pp. 855-866, Aug. 2009.
[9] T.Y. Lin, "Neighborhood Systems-A Qualitative Theory for Fuzzy and Rough Sets," Advances in Machine Intelligence and Soft Computing, vol. 4, pp. 132-155, 1997.
[10] T.Y. Lin, "Granular Computing on Binary Relations I: Data Mining and Neighborhood Systems," Rough Sets in Knowledge Discovery, pp. 107-121, Physica-Verlag, 1998.
[11] A. Bargiela and W. Pedrycz, Granular Computing: An Introduction. Kluwer Academic Publishers, 2002.
[12] A. Skowron and J. Stepaniuk, "Information Granules: Towards Foundations of Granular Computing," Int'l J. Intelligent Systems, vol. 16, pp. 57-85, 2001.
[13] B. Apolloni, W. Pedrycz, S. Bassis, and D. Malchiodi, The Puzzle of Granular Computing. Springer, 2008.
[14] W.Z. Wu, Y. Leung, and J.S. Mi, "Granular Computing and Knowledge Reduction in Formal Contexts," IEEE Trans. Knowledge and Data Eng., vol. 21, no. 10, pp. 1461-1474, Oct. 2009.
[15] H.O. Ghaffari, M. Sharifzadeh, K. Shahriar, and W. Pedrycz, "Application of Soft Granulation Theory to Permeability Analysis," Int'l J. Rock Mechanics and Mining Sciences, vol. 46, no. 3, pp. 577-589, 2009.
[16] Y.Y. Yao, "Granular Computing: Basic Issues and Possible Solutions," Proc. Fifth Joint Conf. Information Sciences, pp. 186-189, 2000.
[17] W. Pedrycz, A. Skowron, and V. Kreinovich, Handbook of Granular Computing. Wiley-Interscience, 2008.
[18] Z. Pawlak, "Rough Sets," Int'l J. Computer and Information Sciences, vol. 11, pp. 341-356, 1982.
[19] Z. Pawalk, Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, 1991.
[20] W. Pedrycz, "A Dynamic Data Granulation through Adjustable Fuzzy Clustering," Pattern Recognition Letters, vol. 29, no. 16, pp. 2059-2066, 2008.
[21] W. Pedrycz and R. Weber, "Special Issue on Soft Computing for Dynamic Data Mining," Applied Soft Computing, vol. 8, no. 4, pp. 1281-1282, 2008.
[22] T.C. Chou and M.C. Chen, "Using Incremental PLSI for Threshold-Resilient Online Event Analysis," IEEE Trans. Knowledge and Data Eng., vol. 20, no. 3, pp. 289-299, Mar. 2008.
[23] J.M. Kang, M.F. Mokbel, S.S. Shekhar, T. Xia, and D. Zhang, "Incremental and General Evaluation of Reverse Nearest Neighbors," IEEE Trans. Knowledge and Data Eng., vol. 22, no. 7, pp. 983-998, July 2010.
[24] F. Altiparmak, E. Tuncel, and H. Ferhatosmanoglu, "Incremental Maintenance of Online Summaries over Multiple Streams," IEEE Trans. Knowledge and Data Eng., vol. 20, no. 2, pp. 216-229, Feb. 2008.
[25] A. An, N. Shan, C. Chan, N. Cercone, and W. Ziarko, "Discovering Rules for Water Demand Prediction: An Enhanced Rough-Set Approach," Eng. Application and Artificial Intelligence, vol. 9, no. 6, pp. 645-653, 1996.
[26] T.R. Li, D. Ruan, W. Geert, J. Song, and Y. Xu, "A Rough Sets Based Characteristic Relation Approach for Dynamic Attribute Generalization in Data Mining," Knowledge-Based Systems, vol. 20, no. 5, pp. 485-494, 2007.
[27] N. Shan and W. Ziarko, "Data-Based Acquisition and Incremental Modification of Classification Rules," Computational Intelligence, vol. 11, no. 2, pp. 357-370, 1995.
[28] W.C. Bang and B. Zeungnam, "New Incremental Learning Algorithm in the Framework of Rough Set Theory," Int'l J. Fuzzy Systems, vol. 1, no. 1, pp. 25-36, 1999.
[29] L.Y. Tong and L.P An, "Incremental Learning of Decision Rules Based on Rough Set Theory," Proc. Fourth World Congress on Intelligent Control and Automation, pp. 420-425, 2002.
[30] Y. Liu, C.F. Xu, and Y.H. Pan, "A Parallel Approximate Rule Extracting Algorithm Based on the Improved Discernibility Matrix," Rough Sets and Current Trends in Computing, vol. 3066, pp. 498-503, Springer, 2004.
[31] S. Guo, Z.Y. Wang, Z.C. Wu, and H.P. Yan, "A Novel Dynamic Incremental Rules Extraction Algorithm Based on Rough Set Theory," Proc. Fourth Int'l Conf. Machine Learning and Cybernetics, pp. 1902-1907, 2005.
[32] Z. Zheng and G.Y. Wang, "RRIA: A Rough Set and Rule Tree Based Incremental Knowledge Acquisition Algorithm," Fundamenta Informaticae, vol. 59, nos. 2/3, pp. 299-313, 2004.
[33] T.L. Tseng, "Quantitative Approaches for Information Modeling," PhD Dissertation, Univ. of Iowa, 1999.
[34] Y.N. Fan, T.L. Tseng, C.C. Chern, and C.C. Huang, "Rule Induction Based on an Incremental Rough Set," Expert Systems with Applications, vol. 36, no. 9, pp. 11439-11450, 2009.
[35] H.M. Chen, T.R. Li, S.J. Qiao, and D. Ruan, "A Rough Sets Based Dynamic Maintenance Approach for Approximations in Coarsening and Refining Attribute Values," Int'l J. Intelligent Systems, vol. 25, no. 10, pp. 1005-1026, 2010.
[36] Y.Y. Yao, "Probabilistic Rough Set Approximations," Int'l J. Approximate Reasoning, vol. 49, no. 2, pp. 255-271, 2008.
[37] Y.Y. Yao and S.K.M. Wong, "A Decision Theoretic Framework for Approximating Concepts," Int'l J. Man-Machine Studies, vol. 37, no. 6, pp. 793-809, 1992.
[38] W. Ziarko, "Variable Precision Rough Set Model," J. Computer and System Sciences, vol. 46, no. 1, pp. 39-59, 1993.
[39] W. Ziarko, "Analysis of Uncertain Information in Framework of Variable Precision Rough Set," Foundation of Computing and Decision Sciences, vol. 18, pp. 381-396, 1993.
[40] G. Xie, J.L. Zhang, K.K. Lai, and L. Yu, "Variable Precision Rough Set for Group Decision-Making: An Application," Int'l J. Approximate Reasoning, vol. 49, no. 2, pp. 331-343, 2008.
[41] M. Ningler, G. Stockmanns, G. Schneider, H.D. Kochs, and E. Kochs, "Adapted Variable Precision Rough Set Approach for EEG Analysis," Artificial Intelligence in Medicine, vol. 47, no. 3, pp. 239-261, 2009.
[42] T.P. Hong, T.T. Wang, and S.L. Wang, "Mining Fuzzy $\beta$ -Certain and $\beta$ -Possible Rules from Quantitative Data Based on the Variable Precision Rough-Set Model," Expert Systems with Applications, vol. 32, no. 1, pp. 223-232, 2007.
[43] L. Wang, Y. Wu, and G.Y. Wang, "An Incremental Rule Acquisition Algorithm Based on Variable Precision Rough Set Model," J. Chongqing Univ. of Posts and Telecomm. (Natural Science), pp. 709-713, vol. 17, no. 6, 2005.
[44] D. Liu, T.R. Li, D. Ruan, and W.L. Zou, "An Incremental Approach for Inducing Knowledge from Dynamic Information Systems," Fundamenta Informaticae, vol. 94, pp. 245-260, 2009.
[45] H.M. Chen, T.R. Li, C.X. Hu, and X.L. Ji, "An Incremental Updating Principle for Computing Approximations in Information Systems While the Object Set Varies with Time," Proc. IEEE Int'l Conf. Granular Computing (GrC), pp. 49-52, 2009.
[46] J.Y. Liang and Z.Z. Shi, "The Information Entropy, Rough Entropy and Knowledge Granulation in Rough Set Theory," Int'l J. Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 12, no. 1, pp. 37-46, 2004.
[47] Y.Y. Yao, "Probabilistic Approaches to Rough Sets," Expert Systems, vol. 20, no. 5, pp. 287-297, 2003.
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