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INTELEK Repository
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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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2016-08-24 11:18:35
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Restricted Document
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12830
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UniSZA
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[1] Z. Pawlak, Rough sets, International Journal of Computer and Information Science, 11 (1982), 341–356. http://dx.doi.org/10.1007/bf01001956 [2] Z. Pawlak and A. Skowron, Rudiments of rough sets, Information Sciences an International Journal, 177 (2007), no. 1, 3–27. http://dx.doi.org/10.1016/j.ins.2006.06.003 [3] I. Düntsch and G. Gediga, Algebraic Aspects of Attribute Dependencies in Information Systems, Fundamenta Informaticae, 29 (1997), no. 1-2, 119-133. [4] I. Düntsch and G. Gediga, Statistical evaluation of rough set dependency analysis, International Journal of Human-Computer Studies, 46 (1997), 589–604. http://dx.doi.org/10.1006/ijhc.1996.0105 [5] I. K. Park and G. S. Choi, Rough set approach for clustering categorical data using information-theoretic dependency measure, Information Systems, 48 (2015), 289-295. http://dx.doi.org/10.1016/j.is.2014.06.008 [6] S. J. Yen and Y. S. Lee, A neural network approach to discover attribute dependency for improving the performance of classification, Expert Systems with Applications, 38 (2011), no. 10, 12328-12338. http://dx.doi.org/10.1016/j.eswa.2011.04.011 [7] W. A. Hassanein and A. A. Elmelegy, Clustering algorithms for categorical data using concepts of significance and dependence of attributes, European Scientific Journal, 10 (2014), no. 3, 381-400. [8] W. Ziarko, Dependencies in Structures of Decision Tables, Lecture Notes in Computer Science, RSEISP, (2007), 113–121. http://dx.doi.org/10.1007/978-3-540-73451-2_13 [9] W. Ziarko, The Discovery, Analysis, and Representation of Data Dependencies in Databases, Knowledge Discovery in Databases, (1991), 195–212. [10] W. Ziarko and N. Shan, Discovering attribute relationships, dependencies and rules by using rough sets, HICSS, 3 (1995), no. 3, 293-299. http://dx.doi.org/10.1109/hicss.1995.375608 [11] D. Molodtsov, Soft set theory-first results, Computers and Mathematics with Applications, 37 (1999), 19–31. http://dx.doi.org/10.1016/s0898-1221(99)00056-5 [12] T. Herawan and M.M. Deris, A direct proof of every rough set is a soft set, Proceedings of the third AMS’09, (2009), 119–124. http://dx.doi.org/10.1109/ams.2009.148 [13] T. Herawan, and M.M. Deris, On multi-soft set construction in information system, Manuscript accepted to appear in LNAI Springer Verlag. [14] R. Feldman, Y. Aumann, A. Amir, A. Zilberstein and W. Klosgen, Maximal association rules: a new tool for mining for keywords co-occurrences in document collections, Proceedings KDD 1997, (1997), 167–170. [15] R. Agrawal, T. Imielinski and A. Swami, Mining association rules between sets of items in large databases, Proceedings of the ACM SIGMOD, 22 (1993), no. 2, 207–216. http://dx.doi.org/10.1145/170036.170072 [16] D.A. Bell, J.W. Guan, D.Y. Liu, Mining Association Rules with Rough Sets, Intelligent Data Mining, Springer Verlag, 5 (2005), 163–184. http://dx.doi.org/10.1007/11004011_8 [17] J.W. Guan, D.A. Bell and D.Y. Liu, The Rough Set Approach to Association Rule Mining, Proceedings of the third IEEE ICDM’03, (2003), 529–532. http://dx.doi.org/10.1109/icdm.2003.1250969 [18] Y. Bi, T. Anderson and S. McClean, A rough set model with ontologies for discovering maximal association rules in document collections, Knowledge-Based Systems, 16 (2003), no.5, 243–251. http://dx.doi.org/10.1016/s0950-7051(03)00025-x [19] Reuters-21578, http://www.research.att.com 2002. [20] R. Feldman, Y. Aumann, A. Amir and M. Fresko, Maximal Association Rules: A Tool for Mining Associations in Text, Journal of Intelligent Information Systems, 25 (2005), no. 3, 333–345. http://dx.doi.org/10.1007/s10844-005-0196-9 [21] X. Hu, Knowledge Discovery in Databases: An Attribute-Oriented Rough Set Approach, PhD Thesis, University of Regina, 1995.
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12830 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12830 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal image/jpeg inches 96 96 norman 1414 771 92 92 2016-08-24 11:18:35 1414x771 7137-01-FH02-FIK-16-06428.jpg UniSZA Private Access Discovering attributes dependency for categorical data set based on soft set theory for better decision making Applied Mathematical Sciences Attribute dependency concludes the association between attributes for better accurate decision making. However, the task involved in identifying the relation between categorical values in data set is a complex process. This main focus of this paper is to determine the attribute dependency in a real world application. The proposed method is based on the notion of mapping inclusion from the soft set theory. The categorical data is transformed to predicate and value set to discover the dependency among the attributes. The result shows that the attribute dependencies obtained are comparable to the rough set approach. 9 130 Hikari Ltd. Hikari Ltd. 6477-6490 [1] Z. Pawlak, Rough sets, International Journal of Computer and Information Science, 11 (1982), 341–356. http://dx.doi.org/10.1007/bf01001956 [2] Z. Pawlak and A. Skowron, Rudiments of rough sets, Information Sciences an International Journal, 177 (2007), no. 1, 3–27. http://dx.doi.org/10.1016/j.ins.2006.06.003 [3] I. Düntsch and G. Gediga, Algebraic Aspects of Attribute Dependencies in Information Systems, Fundamenta Informaticae, 29 (1997), no. 1-2, 119-133. [4] I. Düntsch and G. Gediga, Statistical evaluation of rough set dependency analysis, International Journal of Human-Computer Studies, 46 (1997), 589–604. http://dx.doi.org/10.1006/ijhc.1996.0105 [5] I. K. Park and G. S. Choi, Rough set approach for clustering categorical data using information-theoretic dependency measure, Information Systems, 48 (2015), 289-295. http://dx.doi.org/10.1016/j.is.2014.06.008 [6] S. J. Yen and Y. S. Lee, A neural network approach to discover attribute dependency for improving the performance of classification, Expert Systems with Applications, 38 (2011), no. 10, 12328-12338. http://dx.doi.org/10.1016/j.eswa.2011.04.011 [7] W. A. Hassanein and A. A. Elmelegy, Clustering algorithms for categorical data using concepts of significance and dependence of attributes, European Scientific Journal, 10 (2014), no. 3, 381-400. [8] W. Ziarko, Dependencies in Structures of Decision Tables, Lecture Notes in Computer Science, RSEISP, (2007), 113–121. http://dx.doi.org/10.1007/978-3-540-73451-2_13 [9] W. Ziarko, The Discovery, Analysis, and Representation of Data Dependencies in Databases, Knowledge Discovery in Databases, (1991), 195–212. [10] W. Ziarko and N. Shan, Discovering attribute relationships, dependencies and rules by using rough sets, HICSS, 3 (1995), no. 3, 293-299. http://dx.doi.org/10.1109/hicss.1995.375608 [11] D. Molodtsov, Soft set theory-first results, Computers and Mathematics with Applications, 37 (1999), 19–31. http://dx.doi.org/10.1016/s0898-1221(99)00056-5 [12] T. Herawan and M.M. Deris, A direct proof of every rough set is a soft set, Proceedings of the third AMS’09, (2009), 119–124. http://dx.doi.org/10.1109/ams.2009.148 [13] T. Herawan, and M.M. Deris, On multi-soft set construction in information system, Manuscript accepted to appear in LNAI Springer Verlag. [14] R. Feldman, Y. Aumann, A. Amir, A. Zilberstein and W. Klosgen, Maximal association rules: a new tool for mining for keywords co-occurrences in document collections, Proceedings KDD 1997, (1997), 167–170. [15] R. Agrawal, T. Imielinski and A. Swami, Mining association rules between sets of items in large databases, Proceedings of the ACM SIGMOD, 22 (1993), no. 2, 207–216. http://dx.doi.org/10.1145/170036.170072 [16] D.A. Bell, J.W. Guan, D.Y. Liu, Mining Association Rules with Rough Sets, Intelligent Data Mining, Springer Verlag, 5 (2005), 163–184. http://dx.doi.org/10.1007/11004011_8 [17] J.W. Guan, D.A. Bell and D.Y. Liu, The Rough Set Approach to Association Rule Mining, Proceedings of the third IEEE ICDM’03, (2003), 529–532. http://dx.doi.org/10.1109/icdm.2003.1250969 [18] Y. Bi, T. Anderson and S. McClean, A rough set model with ontologies for discovering maximal association rules in document collections, Knowledge-Based Systems, 16 (2003), no.5, 243–251. http://dx.doi.org/10.1016/s0950-7051(03)00025-x [19] Reuters-21578, http://www.research.att.com 2002. [20] R. Feldman, Y. Aumann, A. Amir and M. Fresko, Maximal Association Rules: A Tool for Mining Associations in Text, Journal of Intelligent Information Systems, 25 (2005), no. 3, 333–345. http://dx.doi.org/10.1007/s10844-005-0196-9 [21] X. Hu, Knowledge Discovery in Databases: An Attribute-Oriented Rough Set Approach, PhD Thesis, University of Regina, 1995.
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| spellingShingle |
Discovering attributes dependency for categorical data set based on soft set theory for better decision making
|
| summary |
Attribute dependency concludes the association between attributes for better accurate decision making. However, the task involved in identifying the relation between categorical values in data set is a complex process. This main focus of this paper is to determine the attribute dependency in a real world application. The proposed method is based on the notion of mapping inclusion from the soft set theory. The categorical data is transformed to predicate and value set to discover the dependency among the attributes. The result shows that the attribute dependencies obtained are comparable to the rough set approach.
|
| title |
Discovering attributes dependency for categorical data set based on soft set theory for better decision making
|
| title_full |
Discovering attributes dependency for categorical data set based on soft set theory for better decision making
|
| title_fullStr |
Discovering attributes dependency for categorical data set based on soft set theory for better decision making
|
| title_full_unstemmed |
Discovering attributes dependency for categorical data set based on soft set theory for better decision making
|
| title_short |
Discovering attributes dependency for categorical data set based on soft set theory for better decision making
|
| title_sort |
discovering attributes dependency for categorical data set based on soft set theory for better decision making
|