Abstract
Rough set theory provides a methodology for data analysis based on the approximation of information systems. It is revolves around the notion of discernibility i.e. the ability to distinguish between objects based on their attributes value. It allows inferring data dependencies that are useful in the fields of feature selection and decision model construction. Since it is proven that every rough set is a soft set, therefore, within the context of soft sets theory, we present a soft set-based framework for partition attribute selection. The paper unifies existing work in this direction, and introduces the concepts of maximum attribute relative to determine and rank the attribute in the multi-valued information system. Experimental results demonstrate the potentiality of the proposed technique to discover the attribute subsets, leading to partition selection models which better coverage and achieve lower computational time than that the baseline techniques.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Pawlak, Z.: Rough sets. International Journal of Computer and Information Science 11, 341–356 (1982)
Pawlak, Z.: Rough Sets - Theoretical Aspect of Reasoning about Data. Kluwer Academic Publisher, Boston (1991)
Mazlack, L.J., He, A., Zhu, Y., Coppock, S.: A Rough Sets Approach in Choosing Partitioning Attributes. In: Proceeding of ICSA 13th International Conference, CAINE 2000, pp. 1–6 (2000)
Parmar, D., Wu, T., Blackhurst, J.: MMR: An Algorithm for Clustering Categorical Data using Rough Set Theory. Data and Knowledge Discovery 63, 879–893 (2007)
Herawan, T., Deris, M.M., Abawajy, J.H.: A Rough Set Approach for Selecting Clustering Attribute. Knowledge Based System 23, 220–231 (2010)
Molodtsov, D.: Soft set theory - First results. Computer and Mathematics with Applications 37, 19–31 (1999)
Maji, P.K., Biswas, R., Roy, A.R.: Fuzzy soft sets. Journal of Fuzzy Mathematics 9, 589–602 (2001)
Maji, P.K., Biswas, R., Roy, A.R.: Soft Set Theory. Computer and Mathematics with Applications 45, 555–562 (2003)
Ali, M.I., Feng, F., Liu, X., Min, W.K., Shabira, M.: On some new operation in soft sets theory. Computer and Mathematics with Applications 57, 1547–1553 (2009)
Maji, P.K., Roy, A.R., Biswas, R.: An application of soft sets in a decision making problem. Computer and Mathematics with Applications 44, 1077–1083 (2002)
Roy, A.R., Maji, P.K.: A fuzzy soft set theoretic approach to decision making problems. Journal of Computational and Applied Mathematics 203, 412–418 (2007)
Kong, Z., Gao, L., Wang, L.: Comment on “A fuzzy soft set theoretic approach to decision making problems”. Journal of Computational and Applied Mathematics 223, 540–542 (2009)
Herawan, T., Rose, A.N.M., Mat Deris, M.: Soft Set Theoretic Approach for Dimensionality Reduction. In: Ślęzak, D., Kim, T.-H., Zhang, Y., Ma, J., Chung, K.-I. (eds.) DTA 2009. CCIS, vol. 64, pp. 171–178. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mamat, R., Herawan, T., Ahmad, N., Deris, M.M. (2012). On Soft Partition Attribute Selection. In: Liu, B., Ma, M., Chang, J. (eds) Information Computing and Applications. ICICA 2012. Lecture Notes in Computer Science, vol 7473. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34062-8_66
Download citation
DOI: https://doi.org/10.1007/978-3-642-34062-8_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34061-1
Online ISBN: 978-3-642-34062-8
eBook Packages: Computer ScienceComputer Science (R0)