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[1]	I. Düntsch and G. Gediga. Guttman algebras and a model checking procedure for Guttman scales. In Ewa Orlowska on Relational Methods in Logic and Computer Science. Springer Verlag, 2018. To appear. [ bib ]
[2]	I. Düntsch, G. Gediga, and H. Wang. Approximation by filter functions. In Hung Son Nguyen, Quang-Thuy Ha, Tianrui Li, and Malgorzata Przybyla-Kasperek, editors, Rough Sets: International Joint Conference, IJCRS 2018, volume 11103 of LNAI, pages 243-256. Springer Verlag, 2018. [ bib \| http ]
[3]	I. Düntsch and G. Gediga. Rough set clustering. In C. M. Henning, M. Meila, F. Murtagh, and R. Rocci, editors, Handbook of Cluster Analysis, volume 8 of Handbooks of Modern Statistical Methods, chapter 5. Chapman & Hall, 2016. [ bib \| .pdf ]
[4]	I. Düntsch and G. Gediga. PRE and variable precision models in rough set data analysis. Transactions in Rough Sets, pages 17-37, 2015. MR3618228. [ bib \| .pdf ]
[5]	I. Düntsch and G. Gediga. Simplifying contextual structures. In Proceedings of the 6th International Conference on Pattern Recognition and Machine Intelligence, volume 9124 of Lecture Notes in Computer Science, pages 23-32, Heidelberg, 2015. Springer Verlag. [ bib \| .pdf ]
[6]	G. Gediga and I. Düntsch. Standard errors of indices in rough set data analysis. In J.F. Peters and A. Skowron, editors, Transactions on Rough Sets Vol. XVII, volume 8375 of Lecture Notes in Computer Science, pages 33-47. Springer Verlag, Heidelberg, 2014. [ bib \| DOI \| .pdf ]
[7]	I. Düntsch and G. Gediga. Weighted λ precision models in rough set data analysis. In Proceedings of the Federated Conference on Computer Science and Information Systems, Wroclaw, Poland, pages 309-316. IEEE, 2012. [ bib ]
[8]	I. Düntsch and G. Gediga. On the gradual evolvement of things. In A. Skowron and Z. Suraj, editors, Rough Sets and Intelligent Systems. Professor Zdzislaw Pawlak in Memoriam, volume 1, chapter 8, pages 247-257. Springer Verlag, Heidelberg, 2012. [ bib ]
[9]	X. Yin, I. Düntsch, and G. Gediga. Quadtree Representation and Compression of Spatial Data. Transactions on Rough Sets, 13:207-238, 2011. [ bib ]
[10]	I. Düntsch and G. Gediga. A Fast Randomisation Test for Rule Significance. In Marcin Szczuka, Marzena Kryszkiewicz, Sheela Ramanna, Richard Jensen, and Qinghua Hu, editors, Rough Sets and Current Trends in Computing, 7th International Conference, RSCTC 2010, volume 6086 of Lecture Notes in Computer Science, pages 386-391. Springer Verlag, 2010. [ bib ]
[11]	X. Yin, I. Düntsch, and G. Gediga. Choosing the root node of a quadtree. In T.Y. Lin, editor, 2009 IEEE International Conference on Granular Computing (GrC 2009), pages 721-726, 2009. [ bib ]
[12]	I. Düntsch, G. Gediga, and A. Lenarcic. Affordance relations. In Hiroshi Sakai, Mihir K. Chakraborty, Aboul Ella Hassanien, Dominik Slezak, and William Zhu, editors, Proceedings of the Twelfth International Conference on Rough Sets, Fuzzy Sets, Data Mining & Granular Computing, volume 5908 of Lecture Notes in Computer Science, pages 1-11. Springer Verlag, 2009. [ bib ]
[13]	I. Düntsch and G. Gediga. Probabilistic granule analysis. In Chien-Chung Chan, Jerzy W. Grzymala-Busse, and Wojciech P. Ziarko, editors, Proceedings of the Sixth International Conference on Rough Sets and Current Trends in Computing (RSCTC 2008), volume 5306 of Lecture Notes in Computer Science, pages 223-231. Springer Verlag, 2008. [ bib ]
[14]	I. Düntsch, G. Gediga, and E. Orlowska. Relational attribute systems II. Transactions on Rough Sets, 7:16-35, 2007. MR2397127. [ bib \| .pdf ]
[15]	H. Wang, I. Düntsch, G. Gediga, and G. Guo. Nearest Neighbours without k. In Barbara Dunin-Keplicz, Andrzej Jankowski, Andrzej Skowron, and Marcin Szczuka, editors, Monitoring, Security, and Rescue Techniques in Multiagent Systems, Advances in Soft Computing, chapter 12, pages 179-189. Springer Verlag, Heidelberg, 2006. [ bib ]
[16]	G. Gediga, I. Düntsch, and J. Adams-Webber. On the direct scaling approach of eliciting aggregated fuzzy information: The psychophysical view. In Scott Dick, Lukasz Kurgan, Petr Musilek, Witold Pedrycz, and Marek Reformat, editors, Proceedings the 2004 Annual Meeting of the North American Fuzzy Information Processing Society, pages 948-953, 2004. [ bib \| .pdf ]
[17]	H. Wang, I. Düntsch, G. Gediga, and A. Skowron. Hyperrelations in version space. International Journal of Approximate Reasoning, 36:223-241, 2004. MR2062628. [ bib \| .html \| .pdf ]
[18]	A. Muir, I. Düntsch, and G. Gediga. Rough set data representation using binary decision diagrams. Revista Real Academia de Ciencias, Serie A, 98:197-213, 2004. MR2136167. [ bib \| .pdf ]
[19]	I. Düntsch and G. Gediga. Approximation operators in qualitative data analysis. In Harri de Swart, Ewa Orlowska, Gunther Schmidt, and Marc Roubens, editors, Theory and Application of Relational Structures as Knowledge Instruments, volume 2929 of Lecture Notes in Computer Science, pages 214-230. Springer-Verlag, Heidelberg, 2003. [ bib ]
[20]	G. Gediga and I. Düntsch. On model evaluation, indices of importance, and interaction values in rough set analysis. In S.K.Pal, L.Polkowski, and A.Skowron, editors, Rough-Neural Computing: Techniques for computing with words, pages 251-276. Physica Verlag, Heidelberg, 2003. [ bib \| .pdf ]
[21]	G. Gediga and I. Düntsch. Maximum consistency of incomplete data via non-invasive imputation. Artificial Intelligence Review, 19:93-107, 2003. [ bib \| .pdf ]
[22]	I. Düntsch and G. Gediga. Modal-style Operators in Qualitative Data Analysis. In Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM'02), pages 155-162, 2002. [ bib \| .html \| .pdf ]
[23]	G. Gediga and I. Düntsch. Skill set analysis in knowledge structures. British Journal of Mathematical and Statistical Psychology, 55:361-384, 2002. MR1949262. [ bib \| .html \| .pdf ]
[24]	G. Gediga and I. Düntsch. Approximation quality for sorting rules. Computational Statistics and Data Analysis, 40:499-526, 2002. MR1926613. [ bib \| .html \| .pdf ]
[25]	G. Gediga, K.-Ch. Hamborg, and I. Düntsch. Evaluation of software systems. In Encyclopedia of Computer Science and Technology, volume 45, pages 127-153. Marcel Dekker, 2002. Also appeared in volume 72 of the Encyclopedia of Library and Information Science (2002), 166-192. [ bib \| .html \| .pdf ]
[26]	H. Wang, I. Düntsch, G. Gediga, and A. Skowron. Hyperrelations in version space (Extended abstract). In SAC '02: Proceedings of the 2002 ACM symposium on Applied computing, pages 514-518, New York, 2002. ACM Press. [ bib \| DOI ]
[27]	I. Düntsch and G. Gediga. A note on the correspondences among entail relations, rough set dependencies, and logical consequence. Journal of Mathematical Psychology, 45:393-401, 2001. MR1836895. [ bib \| .html \| .pdf ]
[28]	I. Düntsch and G. Gediga. Roughian - Rough Information Analysis. International Journal of Intelligent Systems, 46:121-147, 2001. [ bib \| .html \| .pdf ]
[29]	I. Düntsch, G. Gediga, and E. Orlowska. Relational attribute systems. International Journal of Human Computer Studies, 55(3):293-309, 2001. [ bib \| DOI \| .pdf ]
[30]	G. Gediga and I. Düntsch. Rough approximation quality revisited. Artificial Intelligence, 132:219-234, 2001. [ bib \| .pdf ]
[31]	I. Düntsch and G. Gediga. Logical and algebraic techniques for rough set data analysis. In Lech Polkowski, Shusaku Tsumoto, and Tsau Young Lin, editors, Rough set methods and applications: New developments in knowledge discovery in information systems, pages 521-544. Physica Verlag, Heidelberg, 2000. MR1858667. [ bib ]
[32]	I. Düntsch and G. Gediga. Rough set data analysis: A road to non-invasive knowledge discovery. Methodos Publishers (UK), Bangor, 2000. [ bib \| .pdf ]
[33]	I. Düntsch and G. Gediga. Rough set data analysis. In Encyclopedia of Computer Science and Technology, volume 43, pages 281-301. Marcel Dekker, 2000. [ bib \| .html \| .pdf ]
[34]	I. Düntsch and G. Gediga. Sets, relations, functions. Methodos Publishers (UK), Bangor, 2000. [ bib \| http \| .pdf ]
[35]	I. Düntsch, G. Gediga, and S. Nguyen. Rough sets in the KDD process. In Proc. of IPMU 2000, pages 220-226, 2000. [ bib \| .html \| .pdf ]
[36]	G. Gediga and I. Düntsch. Statistical techniques for rough set data analysis. In Lech Polkowski, Shusaku Tsumoto, and Tsau Young Lin, editors, Rough set methods and applications: New developments in knowledge discovery in information systems, pages 545-565. Physica Verlag, Heidelberg, 2000. MR1858668. [ bib \| .html \| .pdf ]
[37]	G. Gediga, K.-Ch. Hamborg, and I. Düntsch. The IsoMetrics usability inventory: An operationalisation of ISO 9241/10. Behaviour and Information Technology, 18:151-164, 2000. [ bib \| .html \| .pdf ]
[38]	H. Wang, I. Düntsch, and G. Gediga. Classificatory Filtering in Decision Systems. International Journal of Approximate Reasoning, 23:111-136, 2000. MR1745005. [ bib \| .html \| .pdf ]
[39]	C. Browne, I. Düntsch, and G. Gediga. IRIS revisited: A comparison of discriminant and enhanced rough set data analysis. In Lech Polkowski and Andrzej Skowron, editors, Rough sets in knowledge discovery, Vol. 2, pages 345-368. Physica-Verlag, 1998. [ bib \| .html \| .pdf ]
[40]	I. Düntsch and G. Gediga. Simple Data Filtering in Rough Set Systems. International Journal of Approximate Reasoning, 18(1-2):93-106, 1998. MR1657478. [ bib \| .html \| .pdf ]
[41]	I. Düntsch and G. Gediga. Grobian. In Lech Polkowski and Andrzej Skowron, editors, Rough sets in knowledge discovery, Vol. 2, pages 555-557. Physica-Verlag, 1998. [ bib ]
[42]	I. Düntsch and G. Gediga. Knowledge structures and their applications in CALL systems. In S. Jager, J. Nerbonne, and A. van Essen, editors, Language teaching and language technology, pages 177-186. Swets & Zeitlinger, 1998. [ bib \| .html \| .pdf ]
[43]	I. Düntsch and G. Gediga. Logical tools for rule based data analysis. In Jan Komorowski, Ivo Düntsch, and Andrzej Skowron, editors, Workshop on Synthesis of Intelligent Agent Systems from Experimental Data, ECAI'98. IOS Press, 1998. [ bib ]
[44]	I. Düntsch and G. Gediga. Feature selection and data prediction by rough entropy. In Hans-Jürgen Zimmermann, editor, 6th European Congress on Intelligent Techniques and Soft Computing EUFIT'98, pages 81-85, Aachen, Germany, 1998. [ bib ]
[45]	I. Düntsch and G. Gediga. Uncertainty measures of rough set prediction. Artificial Intelligence, 106:77-107, 1998. MR1670304. [ bib \| .html \| .pdf ]
[46]	G. Gediga and I. Düntsch. Statistical tools for rule based data analysis. In Jan Komorowski, Ivo Düntsch, and Andrzej Skowron, editors, Workshop on Synthesis of Intelligent Agent Systems from Experimental Data, ECAI'98. IOS Press, 1998. [ bib ]
[47]	I. Düntsch and G. Gediga. Algebraic Aspects of Attribute Dependencies in Information Systems. Fundamenta Informaticae, 29:119-133, 1997. MR1475089. [ bib \| .html \| .pdf ]
[48]	I. Düntsch and G. Gediga. The Rough Set Engine Grobian. In Achim Sydow, editor, Proc. 15th IMACS World Congress, Berlin, volume 4, pages 613-618, Berlin, 1997. Wissenschaft und Technik Verlag. [ bib \| .html \| .pdf ]
[49]	I. Düntsch and G. Gediga. Non-invasive data analysis. In Proc. 8^th Ireland Conference on Artificial Intelligence, Derry, (1997), pages 24-31, 1997. [ bib ]
[50]	I. Düntsch and G. Gediga. Relation restricted prediction analysis. In Achim Sydow, editor, Proc. 15th IMACS World Congress, Berlin, volume 4, pages 619-624, Berlin, 1997. Wissenschaft und Technik Verlag. [ bib \| .html \| .pdf ]
[51]	I. Düntsch and G. Gediga. Statistical Evaluation of Rough Set Dependency Analysis. International Journal of Human-Computer Studies, 46:589-604, 1997. [ bib \| .html \| .pdf ]
[52]	I. Düntsch and G. Gediga. Roughian - Rough Information Analysis (Extended abstract). In Achim Sydow, editor, Proc. 15th IMACS World Congress, volume 4, pages 631-636, Berlin, 1997. Wissenschaft und Technik Verlag. [ bib ]
[53]	I. Düntsch and G. Gediga. On query procedures to build knowledge structures. Journal of Mathematical Psychology, 40(2):160-168, 1996. MR1398596. [ bib \| .html \| .pdf ]
[54]	I. Düntsch and G. Gediga. Rough set dependency analysis in evaluation studies: An application in the study of repeated heart attacks. Informatics Research Reports, 10:25-30, 1995. [ bib \| .html \| .pdf ]
[55]	I. Düntsch and G. Gediga. Skills and knowledge structures. British Journal of Mathematical and Statistical Psychology, 48:9-27, 1995. [ bib \| .html \| .pdf ]

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