Abstract
discernibility matrix and binary discernibility matrix method is easy to understand and design, which has aroused great concern by many scholar. Research shows that the two methods produce a large number of repeated and useless elements (if A is the subset of B, B is the useless element of A) on the fly. These repeated and useless elements occupy a lot of space and will affect the efficiency of the algorithm. If we delete these elements, the storage is much less than before, and the algorithm will be increased. For this purpose, professor Yang Ming give the definition of enriching discernibility matrix [3],which all the discernibility elements are not repetition and mutually exclusive. Some scholars adopt the method of comparison between every two discernibility elements to get the enriching discernibility matrix. Some present the algorithm, every nonempty entry of a discernibility matrix is stored one path in the enriching tree and a lot of nonempty entries share one path or sub-path. However, these algorithms only delete part of the useless elements in spite of lower storage space. In this paper, we put forward discernibility matrix enriching and Boolean- And algorithm for attributes Reduction. The algorithm is easy to understand and easy to design. The Analysis Experiment and Experimental Comparison show the algorithm is feasible and effective.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Pawlak, Z.: Rough sets. Int. J. of Information and Computer Science 11(5), 341–346 (1982)
Pawlak, Z.: Rough set approach to multi-attribute decision analysis. European J. of Operational Research 72(3) (1994)
Yang, M., Yang, P.: Discernibility Matrix Enriching and Computation for Attributes Reduction. Computer Science 33(9), 181–183 (2006)
Yin, Z., Zhang, J.: An attribute algorithm Based on a concentration Boolean matrix. Journal of Harbin Engineering University 30(3), 307–311 (2009)
Wang, J.Y., Gao, C.: Improved Algorithm for Attribute Reduction
Based on Discernihility Matrix. Computer Engineering 35(3), 66–69 (2009)
Huang, L., Xu, Z., Qian, W., Yang, B.: Quick feature reduction algorithm based on improved frequent pattern tree. Computer Engineering and Applications 46(35), 152–191 (2010)
Yang, M., Yang, P.: A novel condensing tree structure for rough set feature selection. Neurocomputing 71(4/5/6), 1092–1100 (2008)
Ananthanarayana, V.S., Narasimha Murty, M., Subramanian, D.K.: Tree structure for efficient data mining using rough sets. Pattern Recognition Letters 24, 851–862 (2003)
Yang, M., Yang, P.: A novel approach to improving, C-Tree for feature selection. Applied Soft Computing 11(2), 1924–1931 (2010)
Yang, M., Lv, J.: An incremental updating algorithm for attribute reduction based on C-Tree. Control and Decision 27(12), 1769–1775 (2012)
Xu, Z., Yang, B., Song, W.: Qucik Attribute Reduction Algorithm Based on Simple Binary Discernibility Matrix. Computer Science 33(4), 155–158 (2006)
Zhi, T., Miao, D.: The Binary Diseernibility Matrix’s Transformation and High Efficiency Attributes Reduction Algorithm. Computer Science 29(2), 140–142 (2002)
Shu, W., Xu, Z., et al.: Quick Attribute Reduction Algorithm Based on Incomplete Decision Table. Joumal of Chinese Computer Systems 32(9), 103–110 (2011)
Xu, Z., Yang, B., Song, W., Hou, W.: Comparative Research of Different Attribute Reduction Definitions. Joumal of Chinese Computer Systems 29(5), 848–853 (2008)
Gao, J., Han, Z.: Construction of decision tree with discernibility matrix. Computer Engineering and Application 47(33), 18–21 (2011)
Kryszkiewicz, M.: Rough set approach to incomplete information system. Information Science 112(1), 39–49 (1998)
Kryszkiewicz, M.: Rules in incomplete information systems. Information Sciences 113(2), 271–292 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Xu, Z., Wang, T., Zhu, J., Zhang, X. (2014). Discernibility Matrix Enriching and Boolean-And Algorithm for Attributes Reduction. In: Luo, X., Yu, J.X., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2014. Lecture Notes in Computer Science(), vol 8933. Springer, Cham. https://doi.org/10.1007/978-3-319-14717-8_52
Download citation
DOI: https://doi.org/10.1007/978-3-319-14717-8_52
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14716-1
Online ISBN: 978-3-319-14717-8
eBook Packages: Computer ScienceComputer Science (R0)