Rough-Set-Based Interrelationship Mining for Incomplete Decision Tables
Yasuo Kudo* and Tetsuya Murai**
*College of Information and Systems, Graduate School of Engineering, Muroran Institute of Technology
27-1 Mizumoto, Muroran 050-8585, Japan
**Faculty of Science and Technology, Chitose Institute of Science and Technology
758-65 Bibi, Chitose 066-8655, Japan
Rough-set-based interrelationship mining enables to extract characteristics by comparing the values of the same object between different attributes. To apply this interrelationship mining to incomplete decision tables with null values, in this study, we discuss the treatment of null values in interrelationships between attributes. We introduce three types of null values for interrelated condition attributes and formulate a similarity relation by such attributes with these null values.
-  Z. Pawlak, “Rough Sets,” Int. J. of Computer and Information Science, Vol.11, pp. 341-356, 1982.
-  Z. Pawlak, “Rough Sets: Theoretical Aspects of Reasoning about Data,” Kluwer Academic Publishers, 1991.
-  Z. Pawlak and A. Skowron, “Rough Sets: Some Extensions,” Information Sciences, Vol.177, pp. 28-40, 2007.
-  M. Kryszkiewicz, “Rough set approach to incomplete information systems,” Information Sciences, Vol.112, pp. 39-49, 1998.
-  Y. Kudo and T. Murai, “Indiscernibility Relations by Interrelationships between Attributes in Rough Set Data Analysis,” Proc. of IEEE GrC 2012, pp. 264-269, 2012.
-  Y. Kudo and T. Murai, “A Plan of Interrelationship Mining Using Rough Sets,” Proc. of the 29th Fuzzy System Symp., pp. 33-36, 2013 (in Japanese).
-  Y. Kudo and T. Murai, “Decision Logic for Rough Set-based Interrelationship Mining,” Proc. of IEEE GrC 2013, pp. 172-177, 2013.
-  Y. Kudo and T. Murai, “Interrelationship Mining from a Viewpoint of Rough Sets on Two Universes,” Proc. of IEEE GrC 2014, pp. 137-140, 2014.
-  Y. Kudo and T. Murai, “Some Properties of Interrelated Attributes in Relative Reducts for Interrelationship Mining,” Proc. of SCIS&ISIS 2014, SOFT, pp. 998-1001, 2014.
-  Y. Kudo, Y. Okada, and T. Murai, “On a Possibility of Applying Interrelationship Mining to Gene Expression Data Analysis,” Brain and Health Informatics, LNAI 8211, pp. 379-388, 2013.
-  Y. Kudo and T. Murai, “A Note on Application of Interrelationship Mining to Incomplete Information Systems,” Proc. of the 31st Fuzzy System Symp., pp. 777-778, 2015 (in Japanese).
-  Y. Y. Yao, B. Zhou, and Y. Chen, “Interpreting Low and High Order Rules: A Granular Computing Approach,” Proc. of RSEISP 2007, LNCS 4585, pp. 371-380, 2007.
-  S. Greco, B. Matarazzo, and R. Słowiŉski, “Rough set theory for multicriteria decision analysis,” European J. of Operational Research, Vol.129, pp. 1-47, 2002.
-  Y. Kudo and T. Murai, “On Representation Ability of Interrelated Attributes in Rough Set-based Interrelationship Mining,” Proc. of ISIS 2015, pp. 1229-1237, 2015.
-  Y. Kudo and T. Murai, “An Evaluation method of Relative Reducts based on Roughness of partitions,” Int. J. of Cognitive Informatics and Natural Intelligence, Vol.4, No.2, pp. 50-62, 2010.
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