Abstract
Covering based rough set, an extension of the traditional rough set theory, which uses the cover set of the universe set instead of the partition of the universe, has proven to be both theoretical and attractive in terms of applications. Corresponding to the decision table in traditional rough set theory, the concept of covering decision system has been defined. In this paper, we propose a decision table type based on covers, including the condition lattice of covers, and the decision lattice of covers. Two tasks on covering based decision table are also introduced. We also demonstrate the applications of the covering based decision table in collaborative filtering that corresponds to the classification in the traditional decision table, and in constraint based association rule mining to indicate this covering decision table concept has a potential application.
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
Similar content being viewed by others
References
Pawlak Z (1981) Rough sets. ICS PAS Reports, vol 431
Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11(5):341–356
Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data. Springer, Netherlands
Skowron A, Dutta S (2018) Rough sets: past, present, and future. Natural Computing, pp 1–22
Yao Y (2020) Three-way granular computing, rough sets, and formal concept analysis. Int J Approx Reason 116:106–125
Zhan J, Zhang X, Yao Y (2020) Covering based multigranulation fuzzy rough sets and corresponding applications. Artif Intell Rev 53(2):1093–1126
Zakowski W (1983) Approximations in the Space (U, Π). Demonstratio Mathematica 16(3):761–769
Yao Y, Yao B (2012) Covering based rough set approximations. Inf Sci 200:91–107
Zhao Z (2016) On some types of covering rough sets from topological points of view. Int J Approximate Reason 68:1–14
Zhu W (2007) Basic concepts in covering-based rough sets. vol 5. ICNC, pp 283–286
Zhang Z, Kudo Y, Murai T (2015) Applying covering-based rough set theory to user-based collaborative filtering to enhance the quality of recommendations. In: IUKM, pp 279–289
Zhang Z, Kudo Y, Murai T (2017) Neighbor selection for user-based collaborative filtering using covering-based rough sets. Ann Oper Res 256(2):359–374
Zhang Z, Kudo Y, Murai T, Ren Y (2020) Improved covering-based collaborative filtering for new users’ personalized recommendations. Knowl Inf Syst 62(8):3133–3154
Degang C, Wang C, Qinghua H (2007) A new approach to attribute reduction of consistent and inconsistent covering decision systems with covering rough sets. Inf Sci 177(17):3500–3518
Chen J, Lin Y, Lin G, Li J, Zhang Y-L (2017) Attribute reduction of covering decision systems by hypergraph model. Knowl Based Syst 118:93–104
Li F, Yin Y (2009) Approaches to knowledge reduction of covering decision systems based on information theory. Inf Sci 179(11):1694–1704
Tan A, Li J, Lin Y, Lin G (2015) Matrix-based set approximations and reductions in covering decision information systems. Int J Approx Reason 59:68–80
Wang C, He Q, Chen D, Qinghua H (2014) A novel method for attribute reduction of covering decision systems. Inf Sci 254:181–196
Lirun S, Zhu W (2017) Closed-set lattice and modular matroid induced by covering-based rough sets. Int J Mach Learn Cybernet 8(1):191–201
Tan A, Li J, Lin G (2015) Connections between covering-based rough sets and concept lattices. Int J Approx Reason 56:43–58
Wang Z, Feng Q, Wang H (2019) The lattice and matroid representations of definable sets in generalized rough sets based on relations. Inf Sci 485:505–520
Pawlak Zd (1981) Information systems-theoretical foundations. Inf Syst 6(3):205–218
Bonikowski Z, Bryniarski E, Wybraniec-Skardowska U (1998) Extensions and intentions in the rough set theory. Info Sci 107(1–4):149–167
Degang C, Zhang W, Yeung DS, Tsang ECC (2006) Rough approximations on a complete completely distributive lattice with applications to generalized rough sets. Inf Sci 176(13):1829–1848
Aggarwal CC (2015) Chapter 5. association pattern mining: advanced concepts. In: Aggarwal CC (ed) Data mining: the textbook. Springer, pp 135–152
Wei L, Qi JJ (2010) Relation between concept lattice reduction and rough set reduction. Knowled-Based Syst 23(8):934–938
Zhu W (2007) Topological approaches to covering rough sets. Info Sci 177(6):1499–1508
Aggarwal CC (2016) In: Recommender systems: the textbook. Springer
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Pham, TH., Nguyen, TCV., Vuong, TH., Ho, T., Ha, QT., Nguyen, TT. (2021). A Definition of Covering Based Decision Table and Its Sample Applications. In: Kim, H., Kim, K.J., Park, S. (eds) Information Science and Applications. Lecture Notes in Electrical Engineering, vol 739. Springer, Singapore. https://doi.org/10.1007/978-981-33-6385-4_17
Download citation
DOI: https://doi.org/10.1007/978-981-33-6385-4_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-6384-7
Online ISBN: 978-981-33-6385-4
eBook Packages: Computer ScienceComputer Science (R0)