"Mining Knowledge Rules from Databases: A Rough Set Approach"

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Book	Author(s)	Year	Publisher
Knowledge Representation Techniques: A Rough Set Approach	Patrick Doherty; Witold Lukaszewicz; Andrzej Szalas	2006	Springer Berlin Heidelberg
Inhibitory Rules in Data Analysis: A Rough Set Approach	Pawel Delimata; Mikhail Ju. Moshkov; Zbigniew Suraj	2009	Springer Berlin Heidelberg
Combinatorial Machine Learning: A Rough Set Approach	Mikhail Moshkov; Beata Zielosko	2011	Springer Berlin Heidelberg
Data Mining: A Knowledge Discovery Approach	Krzysztof J. Cios; Roman W. Swiniarski; Witold Pedrycz; Lukasz A. Kurgan	2007	Springer US
Knowledge acquisition from databases /	Wu, Xindong.	[1995], ©1995.	Ablex Pub.,
Knowledge Discovery from Legal Databases	Andrew Stranieri; John Zeleznikow	2005	Springer Netherlands
Rough – Granular Computing in Knowledge Discovery and Data Mining	J. Stepaniuk	2008	Springer Berlin Heidelberg
Mining Structures of Factual Knowledge from Text: An Effort-Light Approach	Xiang Ren; Jiawei Han		Springer International Publishing
Knowledge Discovery in the Social Sciences: A Data Mining Approach	Xiaoling Shu	2020	University of California Press
Managing and Mining Multimedia Databases	Bhavani Thuraisingham	2001	Routledge
Rough knowledge: poems /	Poreba, Christine,		Anhinga Press
Post-mining of Association Rules: Techniques for Effective Knowledge Extraction	Yanchang Zhao, Yanchang Zhao, Chengqi Zhang, Longbing Cao	2009	Information Science Reference
Action Rules Mining	Agnieszka Dardzinska	2013	Springer Berlin Heidelberg
Data Mining In Time Series Databases	Mark Last, Abraham Kandel, Horst Bunke	2004	World Scientific
Mining and metallurgy: with a set of mining maps	A. P. Keppen	1893	Mining Department
Knowledge Management in Fuzzy Databases	Olga Pons; Maria A. Vila	2000	Physica-Verlag HD
Databases & knowledge systems in merchandising	John M. McCann	1991	Van Nostrand Reinhold
Relational databases and knowledge bases	G. Gardarin	1989	Addison-Wesley
Knowledge Discovery in Multiple Databases	Shichao Zhang; Chengqi Zhang; Xindong Wu	2004	Springer London
Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases	Ashish Ghosh; Satchidananda Dehuri; Susmita Ghosh	2008	Springer Berlin Heidelberg
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Chapter	Author(s)	Book	Book Authors	Year	Publisher
Mining Knowledge Rules from Databases: A Rough Set Approach	X. Hu and N. Cercone	Proceedings of the twelfth International Conference on Data Engineering, February 26-March 1, 1996, New Orleans, Louisiana /	Su, Stanley Y. W.	[1996], ©1996.	IEEE Computer Society Press,
Rules from Belief Networks: A Rough Set Approach	Teresa Mroczek; Jerzy W. Grzymała-Busse; Zdzisław S. Hippe	Rough Sets and Current Trends in Computing: 4th International Conference, RSCTC 2004, Uppsala, Sweden, June 1-5, 2004, Proceedings	Shusaku Tsumoto; Roman Slowiński; Jan Komorowski; Jerzy W. Grzymala-Busse	2004	Springer Berlin Heidelberg
Rules from Belief Networks: A Rough Set Approach		Rough Sets and Current Trends in Computing: 4th International Conference, Rsctc 2004, Uppsala, Sweden, June 1-5, 2004, Proceedings	Tsumoto Sh. (Ed), Slowinski R. (Ed), Komorowski J. (Ed)	2004	Springer
Knowledge Discovery in Medical Multi-databases: A Rough Set Approach	Shusaku Tsumoto	Principles of Data Mining and Knowledge Discovery: Third European Conference, PKDD' 99 Prague, Czech Republic, September 15-18, 1999 Proceedings	Jan Zytkow; Jan Rauch	1999	Springer Berlin Heidelberg
Decision Rules Mining with Rough Set	Haitao Wang; Jing Zhao; Gang Wu; Zhao Chao; Zhang Fan; Xinyu Cao	Advances in Artificial Intelligence, Software and Systems Engineering: Joint Proceedings of the AHFE 2018 International Conference on Human Factors in Artificial Intelligence and Social Computing, Software and Systems Engineering, The Human Side of Service Engineering and Human Factors in Energy, July 21–25, 2018, Loews Sapphire Falls Resort at Universal Studios, Orlando, Florida, USA	Tareq Z. Ahram	1st ed. 2019	Springer International Publishing
Mining Incomplete Data—A Rough Set Approach	Jerzy W. Grzymala-Busse; Zdzislaw S. Hippe	Emerging Paradigms in Machine Learning	Sheela Ramanna; Lakhmi C Jain; Robert J. Howlett	2013	Springer Berlin Heidelberg
Data Mining: A Probabilistic Rough Set Approach	Ning Zhong; Ju-Zhen Dong; Setsuo Ohsuga	Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems	Lech Polkowski	1998	Physica-Verlag HD
Mining Numerical Data—A Rough Set Approach	Jerzy W. Grzymala-Busse	Rough Sets and Intelligent Systems Paradigms: International Conference, RSEISP 2007, Warsaw, Poland, June 28-30, 2007, Proceedings	Marzena Kryszkiewicz; James F. Peters; Henryk Rybinski; Andrzej Skowron	2007	Springer Berlin Heidelberg
Mining Numerical Data – A Rough Set Approach	Jerzy W. Grzymala-Busse	Transactions on Rough Sets XI	James F. Peters	2010	Springer Berlin Heidelberg
Mining Incomplete Data—A Rough Set Approach	Jerzy W. Grzymała-Busse	Rough Set and Knowledge Technology: 6th International Conference, RSKT 2011, Banff, Canada, October 9-12, 2011, Proceedings	JingTao Yao; Sheela Ramanna; Guoyin Wang; Zbigniew Suraj	2011	Springer Berlin Heidelberg
Rough Knowledge Databases	Patrick Doherty; Witold Łukaszewicz; Andrzej Skowron; Andrzej Szałas	Knowledge Representation Techniques: A Rough Set Approach	Patrick Doherty; Witold Lukaszewicz; Andrzej Szalas	2006	Springer Berlin Heidelberg
Rough Set Application. A Rough Set Framework for Mining Propositional Default Rules	T. Mollestad and J. Komorowski.Power System Security Analysis Based on Rough Classification /G. Lambert-Torres, R. Rossi and J. A. Jardini /[et al.].Rough Sets and Principal Component Analysis and Their Applications in Data Model Building and Classification /R. W. Swiniarski.Medical Information Systems - Problems with Analysis and Way of Solutions /K. Slowinski and J. Stefanowski.Induction of Expert Decision Rules Using Rough Sets and Set-Inclusion /S. Tsumoto.Modelling Customer Retention with Statistical Techniques, Rough Data Models, and Genetic Programming /A. E. Eiben, T. J. Euverman and W. Kowalczyk /[et al.]	Rough-fuzzy hybridization: a new trend in decision-making /	Pal, Sankar K.; Skowron, Andrzej.	1999.	Springer Verlag,
Knowledge and Uncertainty a Rough Set Approach	Zdzisław Pawlak	Incompleteness and Uncertainty in Information Systems: Proceedings of the SOFTEKS Workshop on Incompleteness and Uncertainty in Information Systems, Concordia University, Montreal, Canada, 8–9 October 1993	V.S. Alagar; S. Bergler; F.Q. Dong	1994	Springer London
Rough-set inspired approach to knowledge discovery in business databases	W. Kowalczyk; Z. Piasta	Research and Development in Knowledge Discovery and Data Mining: Second Pacific-Asia Conference, PAKDD' 98, Melbourne, Australia, April 15-17, 1998, Proceedings	Xindong Wu; Ramamohanarao Kotagiri; Kevin B. Korb	1998	Springer Berlin Heidelberg
12 Knowledge Mining from Databases		Knowledge Mining Using Intelligent Agents	Satchidananda Dehuri, Sung-Bae Cho	2011	World Scientific
An Attribute-Oriented Rough Set Approach for Knowledge Discovery in Databases	Xiaohua Hu; Nick Cercone; Jiawei Han	Rough Sets, Fuzzy Sets and Knowledge Discovery: Proceedings of the International Workshop on Rough Sets and Knowledge Discovery (RSKD’93), Banff, Alberta, Canada, 12–15 October 1993	Wojciech P. Ziarko	1994	Springer London
Mining of MicroRNA Expression Data—A Rough Set Approach	Jianwen Fang; Jerzy W. Grzymala-Busse	Rough Sets and Knowledge Technology: First International Conference, RSKT 2006, Chongquing, China, July 24-26, 2006, Proceedings	James F. Peters; Yiju Yao	2006	Springer Berlin Heidelberg
A Rough Set Approach to Online Customer’s Review Mining	Wei Wang; Huan Yuan; Ye Chen; Ping Yang; Leilei Ni; Yu Li	Advances in Computer Science and Information Engineering: Volume 1	David Jin; Sally Lin	2012	Springer Berlin Heidelberg
Rough Set Approach to Domain Knowledge Approximation	Tuan Trung Nguyen; Andrzej Skowron	Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: 9th International Conference, RSFDGrC 2003, Chongqing, China, May 26-29, 2003, Proceedings	Guoyin Wang; Qing Liu; Yiyu Yao	2003	Springer Berlin Heidelberg
Data Mining — a Rough Set Perspective	Zdzisław Pawlak	Methodologies for Knowledge Discovery and Data Mining: Third Pacific-Asia Conference, PAKDD' 99, Beijing, China, April 26-28, 1999, Proceedings	Ning Zhong; Lizhu Zhou	1999	Springer Berlin Heidelberg
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Showing 1 to 20 of 100 entries

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