LIST OF PUBLICATIONS 1996-2000

A. DOCTORAL THESES

B. MASTER THESES

C. EDITED BOOKS, PROCEEDINGS, SPECIAL ISSUES OF JOURNALS

D. BOOK AND JOURNAL ARTICLES

E. CONFERENCE PAPERS

F. REPORTS AND MANUSCRIPTS

G. SOFTWARE SYSTEMS

A. DOCTORAL THESES

  1. Jan G. Bazan (1998). Metody wnioskowań aproksymacyjnych dla syntezy algorytmów decyzyjnych. Ph. D. thesis, supervisor A. Skowron, Warsaw University, pp. 1-179.
  2. T. Mollestad (1996). A rough set approach to default rules data mining. Ph.D. Dissertation, supervisor J. Komorowski, Norvegian Institute of Technology, Trondheim, Norway.
  3. Rafał Deja (2000), Zastosowania metod zbiorów przyblizonych w analizie konfliktów. Ph. D. Dissertation, supervisor A. Skowron, Institute of Computer Science PAN.
  4. Hoa S. Nguyen (1999). Data regularity analysis and applications in data mining. Ph. D. thesis, supervisor B. Chlebus, Warsaw University.
  5. Son H. Nguyen (1997). Discretization of real value attributes. Boolean reasoning approach. Ph. D. thesis, supervisor A. Skowron, Warsaw University
  6. Marcin S. Szczuka (1999). Metody symboliczne i sieci neuronowe w konstrukcji klasyfikatorów, (Symbolic methods and artificial neural networks in classifier construction), Ph. D. thesis,Warsaw University
  7. Ibrahim Tentush (1997). On minimal absorbents and closure properties of rough inclusions: new results in rough set theory. Ph. D. thesis, supervisor L. Polkowski, Institute of Computer Science PAN.

B. MASTER THESES

  1. M. Borkowski (2000). Konstruowanie systemów decyzyjnych ze zmienną przestrzenią atrybutów., Master Thesis, supervisor A. Skowron, Institute of Mathematics Warsaw University
  2. A. Cykier (1997). Implikanty pierwsze funkcji boolowskich, metody wyznaczania i zastosowania. Master Thesis, supervisor A. Skowron, Institute of Mathematics Warsaw University
  3. P. Ejdys, G. Góra (1998). Złóżoność indukcyjnego definiowania funkcji boolowskich z przykładów. Master Thesis, supervisor A. Skowron, Institute of Mathematics Warsaw University
  4. P. Bulkowski, P. Ejdys, G. Góra (1999). Projekt implementacji języka do obliczeń rozproszonych. Master Thesis, supervisor A. Skowron, Institute of Mathematics Warsaw University
  5. P. Synak (1997). Metody wnioskowania aproksymacyjnego w wykrywaniu zależności przybliżonych. Master Thesis, supervisor A. Skowron, Institute of Mathematics Warsaw University
  6. D. Ślęzak (1996). Wybrane metody wnioskowań aproksymacyjnych. Master Thesis, supervisor A. Skowron, Institute of Mathematics Warsaw University
  7. J. Wróblewski (1996). Rozwiązywanie niektórych problemów NP-trudnych za pomocą algorytmów genetycznych: podstawy teopretyczne i zastosowania. Master Thesis, supervisor A. Skowron, Institute of Mathematics Warsaw University

C. EDITED BOOKS, PROCEEDINGS, SPECIAL ISSUES OF JOURNALS

  1. J. Komorowski, A. Skowron, I. Duentsch (eds.) (1998). Proceedings of the W8 Workshop at ECAI'98 on Synthesis of Intelligent Agents from Experimental Data. Brighton August 24, 1998.
  2. J. Komorowski, L. Polkowski, A. Skowron (1998). Tutorial T8: Rough sets for data mining and knowledge discovery at ECAI'98. Brighton August 25, 1998.
  3. W. Marek, L. Polkowski and A. Skowron (1996). Fundamenta Informaticae 28/3-4, Special Issue: To the Memory of Professor Helena Rasiowa.
  4. S.K. Pal and A. Skowron (eds.) (1999). Rough Fuzzy Hybridization: A New Trend in Decision-Making. Springer-Verlag, Singapore, ISBN 981-4021-00-8.
  5. L. Polkowski and A. Skowron (eds.) (1998). Rough Sets in Knowledge Discovery 1: Methodology and Applications. Physica-Verlag, Heidelberg, pp. 1-576.
  6. L. Polkowski and A. Skowron (eds.) (1998). Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems. Physica-Verlag, Heidelberg, pp. 1-601.
  7. L. Polkowski and A. Skowron (eds.) (1998). 1st International Conference on Rough Sets and Current Trends in Computing (RSCTC'98). Warsaw, June 2, 22-26, 1998, Lecture Notes in Artificial Intelligence 1424, Springer-Verlag, Heidelberg, pp. 1-626.
  8. Z.W. Ras and A. Skowron (1997). Tenth International Symposium on Methodologies for Intelligent Systems, Foundations of Intelligent Systems (ISMIS-97). October 15-18, Charlotte, NC, USA, Lecture Notes in Artificial Intelligence 1325, Springer - Verlag, Berlin, pp. 1-630.
  9. Z.W. Ras and A. Skowron (1999). Eleventh International Symposium on Methodologies for Intelligent Systems, Foundations of Intelligent Systems (ISMIS-99). June 8-11, Warsaw, Poland, Lecture Notes in Artificial Intelligence 1609, Springer - Verlag, Berlin, pp. 1-676.
  10. A. Skowron (ed.) (1996). Logic, algebra and computer science, Helena Rasiowa and Cecylia Rauszer in Memoriam. Bulletin of the Section of Logic 25(3-4), pp. 1-215.
  11. A. Skowron, W. Ziarko (1996). Fundamenta Informaticae 27/2-3, Special Issue on Rough Sets.
  12. A. Skowron, (with N. Zhong, S. Ohsuga) (1999), New directions in Rough Sets, Data Mining, and Granular Soft Computing. Proceedings of the 7th International Workshop (RSFDGrC'99), November 1999, Yamaguchi, Japan, Lecture Notes in Artificial Intelligence 1711 ISBN 3-540-66645-1.

D. BOOK AND JOURNAL ARTICLES

  1. J.G. Bazan, Son H. Nguyen, Trung T. Nguyen, A. Skowron and J. Stepaniuk (1998). Decision rules synthesis for object classification. In: E. Orowska (ed.), Incomplete Information: Rough Set Analysis, Physica - Verlag, Heidelberg, pp. 23-57.
  2. J.G. Bazan (1998). A Comparison of Dynamic and non-Dynamic Rough Set Methods for Extracting Laws from Decision Table. In: L. Polkowski, A. Skowron (eds.), Rough Sets in Knowledge Discovery, Physica - Verlag, Heidelberg, pp. 321-365.
  3. R. Deja (1997). Conflict model with negotiations. Bull. Polish Acad. Sci. Tech. 44/4, pp. 476-498.
  4. R. Deja (2000). Conflict Analysis. In: L. Polkowski, T.Y. Lin, S. Tsumoto, Rough Sets in Soft Computing and Knowledge Discovery: New Developments, Physica Verlag, Heidelberg.
  5. J. Komorowski, L. Polkowski and A. Skowron (1997). Towards a rough mereology-based logic for approximate solution synthesis, Part 1. Studia Logica 58/1, pp. 143-184.
  6. J. Komorowski, Z. Pawlak, L. Polkowski and A. Skowron (1999). Rough sets: A tutorial. in: S.K. Pal and A. Skowron (eds.), Rough fuzzy hybridization: A new trend in decision-making, Springer-Verlag, Singapore, pp. 3-98.
  7. M. Moshokov (1996). Comparative analysis of deterministic and non-deterministic decision tree complexity. Global approach. Fundamenta Informaticae 25, pp. 201-214.
  8. M. Moshokov (1997). Unimprovable upper bounds on time complexity of decision trees. Fundamenta Informaticae 31, pp. 157-184.
  9. M. Moshkov, I. Chikalov (1997). Bounds on average weighted depth of decision trees. Fundamenta Informaticae 31/1, pp. 145-156.
  10. Hoa S. Nguyen, H. Son Nguyen (1998). Pattern extraction from data. Fundamenta Informaticae 34/1-2, pp. 129-144.
  11. Hoa S. Nguyen, A. Skowron and P. Synak (1998). Discovery of data patterns with applications to decomposition and classfification problems. In: L. Polkowski and A. Skowron (eds.), Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems, Physica-Verlag, Heidelberg, pp. 55-97.
  12. Son H. Nguyen and A. Skowron (1997). Quantization of real value attributes: Rough set and boolean reasoning approach. Bulletin of International Rough Set Society 1/1, pp. 5-16.
  13. Son H. Nguyen (1998). From Optimal Hyperplanes to Optimal Decision Trees. Fundamenta Informaticae 34/1-2, pp. 145-174.
  14. Son H. Nguyen, Hoa S. Nguyen (1998). Discretization Methods in Data Mining. In: L. Polkowski, A. Skowron (eds.): Rough Sets in Knowledge Discovery. Physica-Verlag, Heidelberg, pp. 451-482.
  15. A. Ohrn, J. Komorowski, A. Skowron and P. Synak) (1997). A software system for rough data analysis. Bulletin of the International Rough Set Society 1/2, pp. 58-59.
  16. A. Ohrn, J. Komorowski, A. Skowron and P. Synak (1998). The design and implementation of a knowledge discovery toolkit based on rough sets- The ROSETTA system. In: L. Polkowski and A. Skowron (eds.), Rough Sets in Knowledge Discovery 1: Methodology and Applications, Physica-Verlag, Heidelberg, pp. 376-399.
  17. A. Ohrn, J. Komorowski, A. Skowron and P. Synak (1998). The ROSETTA software system. In: L. Polkowski and A. Skowron (eds.), Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems, Physica-Verlag, Heidelberg, pp. 572-576.
  18. Z. Pawlak and A. Skowron (1996). Logic, algebra and computer science, Helena Rasiowa and Cecylia Rauszer in Memoriam. Bulletin of the Section of Logic 25/3-4, pp. 119-215.
  19. G. Paun, L. Polkowski and A. Skowron (1996). Parallel communicating grammar systems with negotiations. Fundamenta Informaticae 28/3-4, pp. 315-330.
  20. G. Paun, L. Polkowski and A. Skowron (1997). Rough set approximations of languages. Fundamenta Informaticae 32/2, pp. 149-162.
  21. Z. Pawlak, A. Skowron (1999), Rough Sets Rudiments, Bulletin of IRSS 3/3, pp. 67-70
  22. Peters, J.F., Skowron, A., Suraj, Z., Pedrycz, W., Ramanna, S.(1999): Approximate Real-Time Decision Making: Concepts and Rough Fuzzy Petri Net Models, International Journal of Intelligent Systems, Vol. 14, No. 8, 1998, pp. 805-839.
  23. L. Polkowski and A. Skowron (1996). Adaptive decision-making by systems of cooperative intelligent agents organized on rough mereological principles. Intelligent Automation and Soft Computing, An International Journal, 2/2, pp. 121-132.
  24. L. Polkowski and A. Skowron (1996). Rough mereology: A new paradigm for approximate reasoning. Journ. of Approximate Reasoning 15/4, pp. 333-365.
  25. L. Polkowski and A. Skowron (1996). Analytical morphology: Mathematical morphology of decision tables. Fundamenta Informaticae 27/2-3, pp. 255-271.
  26. L. Polkowski and A. Skowron (1997). Synthesis of decision systems from data tables. In: T.Y. Lin, N. Cercone (eds.), Rough sets and data mining: Analysis of imprecise data, Kluwer Academic Publisher, Dordrecht, pp. 259-299.
  27. L. Polkowski and A. Skowron (1997). Decision algorithms: A survay of rough set theoretic methods. Fundamenta Informaticae 30/3-4, pp. 345-358.
  28. L. Polkowski and A. Skowron (1998). Rough mereology and analytical morphology. In: E. Orlowska (ed.), Incomplete Information: Rough Set Analysis, Physica - Verlag, Heidelberg, pp. 399-437.
  29. L. Polkowski and A. Skowron (1998). Rough sets: A perspective. In: L. Polkowski and A. Skowron (eds.), Rough Sets in Knowledge Discovery 1: Methodology and Applications, Physica-Verlag, Heidelberg, pp. 31-56.
  30. L. Polkowski and A. Skowron (1998). Introducing the book. In: L. Polkowski and A. Skowron (eds.), Rough Sets in Knowledge Discovery 1: Methodology and Applications, Physica-Verlag, Heidelberg, pp. 3-9.
  31. L. Polkowski and A. Skowron (1998). Introducing the book. In: L. Polkowski and A. Skowron (eds.), Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems, Physica-Verlag, Heidelberg, pp. 1-9.
  32. L. Polkowski and A. Skowron (1998). Rough mereological foundations for design, analysis, synthesis, and control in distributed systems. Information Sciences - An International Journal 104/1-2, Elsevier Science, New York, pp. 129-156.
  33. L. Polkowski and A. Skowron (1998). Rough mereological approach - A survey. Bulletin of International Rough Set Society 2/1 pp. 1-13.
  34. L. Polkowski and A. Skowron (1999). Grammar systems for distributed synthesis of approximate solutions extracted from experience., In: G. Paun, A.Salomaa (eds.), Grammar Systems for Multiagent Systems, Gordon and Breach Science Publishers, Amsterdam pp. 316-333.
  35. L. Polkowski and A. Skowron (1999). Approximate reasoning about complex objectsin distributed systems. Rough mereological formalization. In: W. Pedrycz and J.F. Peters III (eds.), Advances in Fuzzy Systems - Applications and Theory 16, Computational Intelligence and Software Engineering, World Scientific, pp. 237-267.
  36. L. Polkowski and A. Skowron (1999). Towards adaptive calculus of granules. In: L.A. Zadeh and J. Kacprzyk (eds.), Computing with Words in Information/Intelligent Systems, Springer-Verlag Group (Physica-Verlag), Studies in Fuzziness and Soft Computing 30, pp.201-228.
  37. L. Polkowski and A. Skowron (2000). Rough Mereology in Information systems. A Case Study: Qualitative Spatial Reasoning. In: L. Polkowski, T.Y. Lin, S. Tsumoto (eds.), Rough Sets in Soft Computing and Knowledge Discovery: New Developments, Physica Verlag, Heidelberg.
  38. L. Polkowski and A. Skowron (2000). Rough Sets and Rough Logic: A KDD Perspective. In: L. Polkowski, T.Y. Lin, S. Tsumoto (eds.), Rough Sets in Soft Computing and Knowledge Discovery: New Developments, Physica Verlag, Heidelberg
  39. A. Skowron and Z. Suraj (1996). A parallel algorithm for real-time decision making: A rough set approach. Journal of Intelligent Information Systems 7, pp. 5-28.
  40. A. Skowron and J. Stepaniuk (1996). Tolerance approximation spaces. Fundamenta Informaticae 27/2-3, pp. 245-253.
  41. A. Skowron, J. Stepaniuk, S. Tsumoto(1999). Information granules for spatial reasoning. Bulletin of IRSS 3/4, pp. 147-154
  42. A. Skowron, J. Stepaniuk (2000). Information granules: Towards Foundations for Spatial and Temporal Reasoning. Indian National Science Academy Journal (in print).
  43. J. Stepaniuk (1998). Optimizations of rough set model. Fundamenta Informaticae 36/2-3, pp. 265-283.
  44. J. Stepaniuk (1998). Rough relations and logics. In: L. Polkowski, A. Skowron (eds.), Rough Sets in Knowledge Discovery 1. Methodology and Applications, Physica Verlag, Heidelberg, pp. 248-260.
  45. J. Stepaniuk (1998). Approximation spaces, reducts and representatives. In: L. Polkowski, A. Skowron (eds.), Rough Sets in Knowledge Discovery 2. Applications, Case Studies and Software Systems, Physica-Verlag, Heidelberg, pp. 109-126.
  46. Z. Suraj (1996). Discovery of concurrent data models from experimental tables. Fundamenta Informaticae 28/3-4, pp. 353-376.
  47. Z. Suraj (1998). The synthesis problem of concurrent systems specified by dynamic information systems. In: L. Polkowski and A. Skowron (eds.), Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems, Physica-Verlag, Heidelberg, pp. 418-448.
  48. Suraj, Z (2000). Rough Set Methods for the Synthesis and Analysis of Concurrent Processes, Studies in Fuzziness and Soft Computing, L. Polkowski (ed.), Springer-Verlag Groups (Physica-Verlag), 2000
  49. Z. Suraj (1998). Reconstruction of cooperative information systems under cost constraints: A rough set approach. Journal of Information Sciences 111 (1998), Elsevier Science Publishers, pp. 273-291.
  50. M. Szczuka (1998). Rough Sets and Artificial Neural Networks. In: L. Polkowski and A. Skowron (eds.), Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems, Physica-Verlag, Heidelberg, pp. 449-470.
  51. M. Szczuka (2001), Refining Classifiers with Neural Networks, International Journal of Intelligent Information Systems, 17(1), Wiley Interscience.
  52. M. Szczuka, P. Wojdyłło (2001), Neuro-Wavelet Classifiers for EEG signals based on Rough Set Methods, Neurocomputing Journal, vol. ,Elsevier.
  53. D. Ślęzak (1999). Decomposition and Synthesis of Decision Tables with respect to Generalized Decision Functions. In: S. Pal and A. Skowron (eds.), Rough Fuzzy Hybridization, A New Trend in Decision-Making, Springer-Verlag, Singapore, pp. 110-135.
  54. D. Ślęzak (2000). Various approaches reasoning with frequency-based decision reducts: a survey. In: L. Polkowski, T.Y. Lin, S. Tsumoto, Rough Sets in Soft Computing and Knowledge Discovery: New Developments, Physica Verlag, Heidelberg.
  55. J. Wróblewski (1996). Theoretical Foundations of Order-Based Genetic Algorithms. Fundamenta Informaticae 28/3-4, pp. 423-430.
  56. J. Wróblewski (1998). Genetic algorithms in decomposition and classification problem. In: L. Polkowski and A. Skowron (eds.), Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems, Physica-Verlag, Heidelberg, pp. 471-487
  57. L. Polkowski (1999) Approximate mathematical morphology. Rough set approach. In: S. Pal and A. Skowron (eds.), Rough Fuzzy Hybridization, A New Trend in Decision-Making, Springer-Verlag, Singapore, pp. 110-135.
  58. S. Radev (1996) Argumentation Systems. Fundamenta Informaticae vol. 28 (3-4), pp. 331-346

E. CONFERENCE PAPERS

  1. J.G. Bazan (1996). Dynamic reducts and statistical inference. In: Proceedings of the Sixth International Conference, Information Procesing and Management of Uncertainty in Knowledge-Based Systems (IPMIU'96) vol. III, July 1-5, Granada, Spain, vol. III, pp. 1147-1152.
  2. J.G. Bazan (1998). Discovery of Decision Rules by Matching New Objects Against Data Tables. In: L. Polkowski, A. Skowron (eds.), Proceedings of the First International Conference on Rough Sets and Current Trends in Computing (RSCTC-98), June 22-26, Warsaw, Poland, pp. 521-528.
  3. J.G. Bazan (1999). Approximate reasoning in decision rule synthesis. Proceedings of the Workshop on Robotics, Intelligent Control and Decision Support Systems, February 22-23, Polish-Japanese Institute of Information Technology, Warsaw, pp. 10-15.
  4. B. Chlebus, Hoa S. Nguyen (1998). On finding optimal discretization on two attributes. In: L. Polkowski, A. Skowron (eds.), Proc. of the first International Conference on Rough Sets and Current Trend in Computing (RSCTC'98), June 1998, Warsaw, Poland, pp. 537-5544.
  5. R. Deja (1996). Conflict analysis. In: S. Tsumoto, S. Kobayashi, T. Yokomori, H. Tanaka and A. Nakamura (eds.), The fourth International Workshop on Rough Sets, Fuzzy Sets, and Machnine Discovery, Proceedings (RS96FD), November 6-8, The University of Tokyo, pp. 118-124.
  6. P. Ejdys and G. Góra (1999). The More We Learn the Less We Know? - On Inductive Learning from Examples. Proceedings of the Eleventh International Symposium on Methodologies for Intelligent Systems, Foundations of Intelligent Systems (ISMIS'99), June 8-11, Warsaw, Lecture Notes in Artificial Intelligence, Springer - Verlag, Berlin (in print).
  7. J. Komorowski, L. Polkowski and A. Skowron (1997). Rough Sets for Data Mining and Knowledge Discovery (Tutorial- abstract). In: J. Komorowski, J. Żytkow (eds.), Proc. of The 1-st European Symposium on Principles of Data Mining and Knowledge Discovery, Trondheim, Norway, June 1997, Lecture Notes in Artificial Intelligence 1263, Springer - Verlag, Berlin, pp. 395-395.
  8. J. Komorowski, L. Polkowski and A. Skowron (1996). Learning tolerance relations by boolean descriptors: Automatic feature extraction from data tables. In: S. Tsumoto, S. Kobayashi, T. Yokomori, H. Tanaka and A. Nakamura (eds.), RSFD'96: The Fourth International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery, University of Tokyo, November 6-8, pp. 11-17.
  9. M. Moshkov (1997). Rough analysis for tree programs. In: Proceedings of the Fifth European Congress on Intelligent Techniques and Soft Computing (EUFIT'97), September 9-11, Aachen, Germany, Verlag Mainz, pp. 231-235.
  10. M. Moshokov (1998). Some relationships between decision trees and decisioon rule systems. In: L. Polkowski, A. Skowron (eds.), Proc. of the first International Conference on Rough Sets and Current Trend in Computing (RSCTC'98), June 1998, Warsaw, Poland, pp. 499-505.
  11. M. Moshokov and I. Chikalov (1997). Bounds on average depth of decision trees. In: Proceedings of the Fifth European Congress on Intelligent Techniques and Soft Computing (EUFIT'97), September 9-11, Aachen, Germany, Verlag Mainz, pp. 226-230.
  12. A. Moshkova and M. Moshkov (1997). Optimal bases for some closed classes of Boolean functions. In: Proceedings of the Fifth European Congress on Intelligent Techniques and Soft Computing (EUFIT'97), September 9-11, Aachen, Germany, Verlag Mainz, pp. 1643-1648.
  13. T. Mollestad and A. Skowron (1996). A rough set framework for data mining of propositional default rules. In: Z.W. Ras and M. Michalewicz (eds.), ISMIS-96: Ninth International Symposium on Methodologies for Intelligent Systems, Zakopane, Poland, June 10-13, Lecture Notes in Artificial Intelligence 1079, Springer - Verlag, Berlin, pp. 448-457.
  14. Hoa S. Nguyen, Trung T. Nguyen, L. Polkowski, A. Skowron, P. Synak and J. Wróblewski (1996). Decision Rules for Large Data Tables. in: P.Borne, G. Dauphin-Tanguy. C. Sueur and S. El Khattabi (eds.), CESA'96: Proceedings of CESA'96 IMACS Multiconference: Computational Engineering in Systems Applications 3/4, July 9-12, Lille, France, Gerf EC Lille - Cite Scientifique, pp. 942-947.
  15. Hoa S. Nguyen, Trung T. Nguyen, A. Skowron and P. Synak (1996). Knowledge discovery by rough set methods. In: Nagib C. Callaos (eds.), ISAS-96: Proc. of the International Conference on Information Systems Analysis and Synthesis, July 22-26, Orlando, USA, pp. 26-33.
  16. Hoa S. Nguyen, A. Skowron and P. Synak (1996). Rough Sets in Data Mining: Approximate Description of Decision Classes. Proc. of EUFIT-96: The fourth European Congress on Intelligent Techniques and Soft Computing, Aachen, September 2-5, Verlag Mainz, pp. 149-153.
  17. Hoa S. Nguyen, Son H. Nguyen (1996). Some efficient algorithms for rough set methods. In: Proceedings of the Sixth International Conference, Information Procesing and Management of Uncertainty in Knowledge-Based Systems (IPMU'96) 2, July 1-5, Granada, Spain pp. 1451-1456.
  18. Hoa S. Nguyen, A. Skowron (1997). Searching for relational patterns in data. In: Komorowski, J., Żytkow, J. (eds.), Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD'97). Trondheim, Norway, June 25-27, Lecture Notes in Artificial Intelligence 1263, Springer-Verlag, Berlin, pp. 265-276.
  19. Hoa S. Nguyen, A. Skowron, P. Synak and J. Wróblewski (1997). Knowledge discovery in data bases: Rough set approach. In: M. Mares, R. Meisar, V. Novak, and J. Ramik (eds.), Proceedings of the Seventh International Fuzzy Systems Assotiation World Congress (IFSA'97)2, June 25-29, Academia, Prague, pp. 204-209.
  20. Hoa S. Nguyen, Son H. Nguyen (1998). Pattern Extraction from Data. In: Proceedings of the Conference of Information Processing and Management of Uncertainty in Knowledge-Based Systems IPMU'98, July 1998, Paris, France, pp. 1346-1353.
  21. Son H. Nguyen, Hoa S. Nguyen and A. Skowron (1996). Searching for features defined by hyperplanes. In: Z.W. Ras and M. Michalewicz (eds.), ISMIS-96: Ninth International Symposium on Methodologies for Intelligent Systems, Zakopane, Poland, June 10-13, Lecture Notes in Artificial Intelligence 1079, Springer - Verlag, Berlin, pp. 366-375.
  22. Son H. Nguyen, Hoa S. Nguyen (1996). Discretization of real value attributes for control problems. In: Proceedings of the Fourth European Congress on Intelligent Techniques and Soft Computing (EUFIT'96) 1, September 2-5, Aachen, Germany, Verlag Mainz (1996) pp. 188-191.
  23. Son H. Nguyen, Hoa S. Nguyen (1996). From optimal hyperplanes to optimal decision trees: rough set and Boolean reasoning approach. In: S. Tsumoto, S. Kobayashi, T. Yokomori, H. Tanaka and A. Nakamura (eds.), The Fourth International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery (RSFD'96), University of Tokyo, November 6-8, 1996, pp. 82-88.
  24. Son H. Nguyen, Hoa S. Nguyen (1997). Discretization methods with back-tracking. In: Proceedings of the Fifth European Congress on Intelligent Techniques and Soft Computing, (EUFIT'97), September 8-11, Aachen, Germany, Verlag Mainz, pp. 201-205.
  25. Son H. Nguyen, Hoa S. Nguyen (1999). An Application of discretization methods in control. Proceedings of the Workshop on Robotics, Intelligent Control and Decision Support Systems, February 22-23, Polish-Japanese Institute of Information Technology, Warsaw, pp. 47-52.
  26. Son H. Nguyen, A. Skowron (1997). Boolean reasoning for feature extraction problems. In: Z.W. Ras, A. Skowron (eds.), Tenth International Symposium on Methodologies for Intelligent Systems, Foundations of Intelligent Systems (ISMIS'97), October 15-18, Charlotte, NC, USA, Lecture Notes in Artificial Intelligence 1325, Springer-Verlag, Berlin, pp. 117-126.
  27. Son H. Nguyen (1997). Rule induction from continuous data. In: Proceedings of the Fifth International Workshop on Rough Sets and Soft Computing (RSSC'97) at Third Annual Joint Conference on Information Sciences (JCIS'97), Duke University, Durham, NC, USA, Rough Set & Computer Science 3, March 1-5, pp. 81-84.
  28. Son H. Nguyen, M. Szczuka and D. Ślęzak (1997). Neural network design: Rough set approach to real-valued data. In: J. Komorowski, J. Żytkow, (eds.), The First European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD'97), June 25-27, Trondheim, Norway, Lecture Notes in Artificial Intelligence 1263, Springer-Verlag, Berlin, pp. 359-366.
  29. Son H. Nguyen (1998). Discretization Problems for Rough Set Methods. In: L. Polkowski, A. Skowron (eds.), Proc. of the first International Conference on Rough Sets and Current Trend in Computing (RSCTC'98), June 1998, Warsaw, Poland, pp. 545-552.
  30. Son H. Nguyen and A. Skowron (1998). Task decomposition problem in multi-agent system. In: Proc. of the Workshop on Concurrency, Specification &Programming, Berlin, September 28-30, 1998, Humboldt-Universität zu Berlin, Informatik Bericht 110, pp. 221-235.
  31. Son H. Nguyen, Hoa S. Nguyen and A. Skowron (1999). Decomposition of Task Specifications. Proceedings of the Eleventh International Symposium on Methodologies for Intelligent Systems, Foundations of Intelligent Systems (ISMIS'99), June 8-11, Warsaw, Lecture Notes in Artificial Intelligence 1609, Springer - Verlag, Berlin, pp. 310-318.
  32. H. Wang and Son H. Nguyen(1999). Text classification using Lattice Machine. Proceedings of the Eleventh International Symposium on Methodologies for Intelligent Systems, Foundations of Intelligent Systems (ISMIS'99), June 8-11, Warsaw, Lecture Notes in Artificial Intelligence 1609, Springer - Verlag, Berlin, pp. 235-243.
  33. Nguyen Hung Son (1999). Efficient SQL-Querying Method for Data Mining in Large Data Bases. Proc. of Sixteenth International Joint Conference on Artificial Intelligence, IJCAI-99, Morgan Kaufmann Publishers, Stockholm, Sweden, pp. 806-811.
  34. Nguyen Hung Son and Nguyen Sinh Hoa (1999). DISCRETIZATION OF REAL VALUE ATTRIBUTES FOR CONTROL PROBLEMS. Workshop on Robotics, Inteligent Control, and Decision Support systems. PJWSTK, Warsaw, Poland.
  35. Son H. Nguyen, A. Skowron (1999). Boolean Reasoning Scheme with some Applicationsin Data Mining, Proceedings of PKDD'99, Prague, Czech Republic, LNAI 1704, Springer - Verlag, Berlin, pp. 107-115.
  36. Hoa S. Nguyen (1999). Discovery of generalized patterns. Proceedings of the Eleventh International Symposium on Methodologies for Intelligent Systems, Foundations of Intelligent Systems (ISMIS'99), June 8-11, Warsaw, Lecture Notes in Artificial Intelligence 1609, Springer - Verlag, Berlin, pp. 574-582.
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  38. J.E. Peters, A. Skowron, Z. Suraj, S. Ramanna and A. Paryzek) (1998). Modelling real - time decision - making systems with rough fuzzy Petri nets. In: Proceedings of the Sixth European Congress on Intelligent Techniques and Soft Computing (EUFIT'98) 2, Aachen, September 1998, Verlag Mainz, Aachen, pp. 985-989.
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  45. L. Polkowski and A. Skowron (1996). Learning synthesis scheme in intelligent systems. In: R.S. Michalski and J. Wnek (eds.), Proceedings of the Third International Workshop on Multistrategy Learning (MSL-96), Harpers Ferry, West Virginia, May 23-25, George Mason University and AAAI Press 1996, pp. 57-68.
  46. L. Polkowski and A. Skowron (1996). Implementing fuzzy containment via rough rough inclusions: rough mereological approach to distributed problem solving. In: Proceedings of the Fourth IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'96), September 8-11, New Orlean, pp. 1147-1153.
  47. L. Polkowski and A. Skowron (1997). Approximate reasoning in distributed systems. In: Proceedings of the Fifth European Congress on Intelligent Techniques and Soft Computing (EUFIT'97), September 9-11, Aachen, Germany, Verlag Mainz, pp. 1630-1633.
  48. L. Polkowski and A. Skowron (1997). Mereological foundations for approximate reasoning in distributed systems (plenary lecture). In: Proceedings of the Second Polish Conference on Evolutionary Algorithms and Global Optimization, Rytro, September 15-19, 1997, pp. 229-236.
  49. L. Polkowski (1998). Rough set approach to mathematical morphology: Approximate compression of data. In: Proceedings of the Seventh International Conference on Information Processing and Management of Uncertainty in Knowledge - Based Systems (IPMU'98), Paris, France, July 6-10, 1998, pp. 1183-1189.
  50. L. Polkowski and A. Skowron (1998). Towards adaptive calculus of granules. In: 1998 IEEE World Congress on Computational Intelligence, Proceedings of the FUZZ-IEEE International Conference, Anchorage, Alaska, USA, May 5-9, 1998, pp. 111-116.
  51. L. Polkowski and A. Skowron (1998). Synthesis of complex objects: Rough mereological approach. In: Proceedings of the W8 Workshop at ECAI'98 on Synthesis of Intelligent Agents from Experimental Data, Brighton, August 24, 1998, pp. 1-10 .
  52. L. Polkowski and A. Skowron (1998). Calculi of granules for adaptive distributed synthesis of intelligent agents founded on rough mereology. In: In: Proceedings of the Sixth European Congress on Intelligent Techniques and Soft Computing (EUFIT'98), Aachen, September 1998, Verlag Mainz, Aachen, pp. 90-93.
  53. L. Polkowski and A. Skowron (1998). Towards information granule calculus., In: Proc. of the Workshop on Concurrency, Specification &Programming, Berlin, September 28-30, 1998, Humboldt-Universität zu Berlin, Informatik Bericht 110, pp. 176-194.
  54. L. Polkowski and A. Skowron (1999). Calculi of granules based on rough set theory. Proceedings of RSFDGrC'99, Yamaguchi, Japan, LNAI 1711, Springer Verlag, Berlin, pp. 20-28
  55. L. Polkowski and A. Skowron (1999). Local calculi of rough-set granulesin problems of approximate synthesis in distributed systems. Proceedings of ICCIMA'99, India
  56. A. Skowron and J. Stepaniuk (1997). Information Reduction Based on Constructive Neighborhood Systems. In: Proceedings of the Fifth International Workshop on Rough Sets Soft Computing (RSSC'97) at Third Annual Joint Conference on Information Sciences (JCIS'97). Duke University, Durham, NC, USA, pp. 158-160.
  57. A. Skowron and J. Stepaniuk (1997). Constructive Information Granules. In: Proceedings of the 15th IMACS World Congress on Scientific Computation, Modelling and Applied Mathematics, Artificial Intelligence and Computer Science 4, August 24-29, 1997, Berlin, Germany, pp. 625-630.
  58. A. Skowron and J. Stepaniuk (1998). Information granules and approximation spaces. In: Proceedings of the Seventh International Conference on Information Processing and Management of Uncertainty in Knowledge - Based Systems (IPMU'98), Paris, France, July 6-10, 1998, pp. 1354-1361.
  59. A. Skowron and J. Stepaniuk (1999). Towards discovery of information granules. Proc. of PKDD'99, Prague, Czech Republic, LNAI 1704, Springer-Verlag, Berlin Heidelberg , pp. 542-547
  60. A. . Skowron and J. Stepaniuk (1999). Information granules in distributed systems. Proceedings of RSFDGrC'99, Yamaguchi, Japan, LNAI 1711, Springer Verlag, Berlin, pp. 357-365
  61. J. Stepaniuk (1996). Rough sets, discretization of attributes and stock market data. Fourth European Congress on Intelligent Techniques and Soft Computing, Proceedings EUFIT'96 , September 2-5, Aachen, Germany, Verlag Mainz, pp.202-203.
  62. J. Stepaniuk (1997). Rough sets similarity based learning. Fifth European Congress on Intelligent Techniques and Soft Computing, Proceedings EUFIT'97 , September 9-11, Aachen, Germany, Verlag Mainz, pp.1634-1639
  63. J. Stepaniuk (1997). Rough sets and similarity relations. Symulacja w badaniach i rozwoju, Trzecie Warsztaty Naukowe PTSK, Wigry, 26-28 września, 1996, (red.) R. Bogacz, L. Bobrowski, Warszawa 1997, 405-413.
  64. J. Stepaniuk (1997). Similarity relations and rough set model. Proceedings of the MENDEL'97, June 25-27, Brno, Czech Republic.
  65. J. Stepaniuk (1997). Attribute discovery and rough sets. In: Komorowski, J., Żytkow, J. (eds.), Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD'97). Trondheim, Norway, June 25-27, Lecture Notes in Artificial Intelligence 1263, Springer-Verlag, Berlin, pp. 145-155.
  66. J. Stepaniuk (1997). Searching for optimal approximation spaces. Proceedings of the Sixth International Workshop on Intelligent Information Systems, June 9-13, 1997, Zakopane, Institute of Computer Science, Polish Academy of Sciences, pp. 86-95.
  67. J. Stepaniuk (1998). Approximation spaces in extensions of rough set theory. Proceedings of the International Conference on Rough Sets and Current Trends in Computing, Warsaw, Poland, June 22-26, 1998, Lecture Notes in Artificial Intelligence 1424, Springer-Verlag, pp. 290-297.
  68. J. Stepaniuk (1998). Rough set based data mining in diabetes mellitus data table. Proceedings of the Sixth European Congress on Intelligent Techniques and Soft Computing (EUFIT'98)2, Aachen, Germany, September 7-10, 1998, pp. 980-984;
  69. J. Stepaniuk (1999). Rough Set Data Mining of Diabetes Data. Proceedings of the Eleventh International Symposium on Methodologies for Intelligent Systems, Foundations of Intelligent Systems (ISMIS'99), June 8-11, Warsaw, Lecture Notes in Artificial Intelligence 1609, Springer - Verlag, Berlin, pp. 457-465
  70. M. Szczuka (1996). Rough set methods for constructing neural networks. In: Proceedings of the Third Biennal Joint Conference On Engineering Systems Design Analysis, Session on Expert Systems, Montpellier, France, pp. 9-14.
  71. M. Szczuka (1998). Refining decision classes with neural networks. In: Proceedings of the Seventh International Conference on Information Processing and Management of Uncertainty in Knowledge - Based Systems (IPMU'98), Paris, France, July 6-10, 1998, pp. 1370-1375.
  72. M. Szczuka, D. Ślęzak, S. Tsumoto (1997). An application of reduct networks to medicine - chaining decision rules. In: Proceedings of the Fifth International Workshop on Rough Sets Soft Computing (RSSC'97) at Third Annual Joint Conference on Information Sciences (JCIS'97). Duke University, Durham, NC, USA, 1997, pp. 395-398.
  73. M. Szczuka (1999), Rules as attributes in classifier construction, Proceedings of RSFDGrC'99, Yamaguchi, Japan, LNAI 1711, Springer Verlag, Berlin, pp. 492-499
  74. D. Ślęzak (1996). Approximate reducts in decision tables. In: Proceedings of the Sixth International Conference, Information Procesing and Management of Uncertainty in Knowledge-Based Systems (IPMU'96), July 1-5, Granada, Spain, 1996, 3, pp. 1159-1164.
  75. D. Ślęzak (1996). Tolerance dependency model for decision rules generation. In: S. Tsumoto, S. Kobayashi, T. Yokomori, H. Tanaka, and A. Nakamura (eds.), Proceedings of the Fourth International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery (RSFD'96), The University of Tokyo, November 6-8, 1996, pp. 131-138.
  76. D. Ślęzak (1997). Rough set reduct networks. In: Proceedings of the Fifth International Workshop on Rough Sets Soft Computing (RSSC'97) at Third Annual Joint Conference on Information Sciences (JCIS'97). Duke University, Durham, NC, USA, 1997, pp. 77-81.
  77. D. Ślęzak (1997). Attribute set decomposition of decision tables. In: Proceedings of the Fifth European Congress on Intelligent Techniques and Soft Computing (EUFiT'97), September 8-12, Aachen, Germany, Verlag Mainz, 1, pp. 236-240.
  78. D. Ślęzak (1997). Decision value decomposition of data tables. In: Z.W. Ras, A. Skowron (eds.), Tenth International Symposium on Methodologies for Intelligent Systems, Foundations of Intelligent Systems (ISMIS'97), October 15-18, Charlotte, NC, USA, Lecture Notes in Artificial Intelligence 1325, Springer-Verlag, Berlin, 1997, pp. 487-496.
  79. D. Ślęzak (1998). Searching for Dynamic Reducts in Inconsistent Decision Tables. In: Proceedings of the Seventh International Conference on Information Processing and Management of Uncertainty in Knowledge - Based Systems (IPMU'98), Paris, France, July 6-10, 1998, pp. 1362-1369.
  80. D. Ślęzak (1998). Searching for frequential reducts in decision tables with uncertain objects. In: L. Polkowski, A. Skowron (eds.), Proceedings of the First International Conference on Rough Sets and Current Trends in Computing (RSCTC'98), June 22-26, Warsaw, Poland. Springer-Verlag, Berlin Heidelberg, pp. 52-59.
  81. Decision information functions for inconsistent decision tables analysis. In: Proceedings of the Seventh European Congress on Intelligent Techniques & Soft Computing, September 13-16, Aachen, Germany p. 127.
  82. D. Ślęzak, Nguyen Hung Son (1999). Approximate Reducts and Association Rules - Correspondence and Complexity Results, Proceedings of RSFDGrC'99, Yamaguchi, Japan, LNAI 1711, Springer Verlag, Berlin, pp. 137-145
  83. D. Ślęzak (2000). Foundations of Entropy Based Bayesian Networks. Accepted for IPMU'2000, Madrid, Spain
  84. D. Ślęzak, M. Szczuka (1997). Hyperplane-based neural networks for real-valued decision tables. In: Proceedings of the Fifth International Workshop on Rough Sets Soft Computing (RSSC'97) at Third Annual Joint Conference on Information Sciences (JCIS'97). Duke University, Durham, NC, USA, pp. 265-268.
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  86. D. Ślęzak, J. Wróblewski (2000). Application of normalized decision measures to the new case classification. Submitted to RSCTC 2000.
  87. Z. Suraj (1997). An application of rough-set methods to cooperative information system re-engineering. In: S. Tsumoto, S. Kobayashi, T. Yokomori, H. Tanaka, and A. Nakamura (eds.), Proceedings of the Fourth International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery (RSFD'96), The University of Tokyo, November 6-8, 1996, pp. 364-371.
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  93. J. Wróblewski (2000). Analyzing relational databases using rough set based methods. Submitted to IPMU 2000, Madrid, Spain
  94. J. Wróblewski (2000), Ensembles of classifiers based on approximate reducts. Submitted to PKDD 2000, Lyon, France
  95. P. Wojdyłło (1998). Wavelets, rough sets and artificial neural networks in EEG analysis. In: L. Polkowski, A. Skowron (eds.), Proceedings of the First International Conference on Rough Sets and Current Trends in Computing (RSCTC'98), June 22-26, Warsaw, Poland. Springer-Verlag, Berlin Heidelberg, pp. 444-449.
  96. J. Stepaniuk (1997). Conflict analysis and groups of agents. In: Z.W. Ras, A. Skowron (eds.), Tenth International Symposium on Methodologies for Intelligent Systems, Foundations of Intelligent Systems (ISMIS'97), October 15-18, Charlotte, NC, USA, Proc. of the poster session.
  97. W. Bartol, X. Caicedo, F. Rosello (1998) Syntactical content of Finite Approximations of Partial Algebras, In: L. Polkowski, A. Skowron (eds.), Proceedings of the First International Conference on Rough Sets and Current Trends in Computing (RSCTC'98), June 22-26, Warsaw, Poland. Springer-Verlag, Berlin Heidelberg, pp. 408-415.

F. REPORTS AND MANUSCRIPTS

  1. R. Deja (1999). Conflict analysis. Technical report (manuscript).
  2. D. Ślęzak (1999). Reasoning in decision tables with frequency based implicants. Technical report (manuscript).
  3. M. Moshokov (1996). On complexity of decision trees over infinite information systems. (manuscript).
  4. P. Synak (1996). Rough Set Expert System - Users's Guide. Technical report (manuscript).
  5. P. Synak, J.G. Bazan, A. Cykier (1997). RSES core classes. Technical Documentation (manuscript).
  6. P. Ejdys, G. Góra (1998). System enabling synthesis of complex objects based on rough mereology. Technical Documentation (manuscript).
  7. P. Bulkowski, P. Ejdys, G. Góra (1998). PSL language guide. Technical Documentation (manuscript).
  8. A. Ohrn, J. Komorowski, A. Skowron and P. Synak (1997). The ROSETTA software system Part I - System overview. Technical Documentation (manuscript).
  9. A. Skowron, J. Komorowski, L. Polkowski (1999). Rough sets: An introduction. 11-th European Summer School in Logic Language and Information, Untrecht University, pp.1-112.
  10. Suraj, Z.(1995): PN-tools: environment  for the design and analysis of Petri nets, Control and Cybernetics, Systems Research Institute of the Polish Academy of Sciences, Vol. 24, No. 2, 1995, pp. 199-222.
  11. Suraj, Z. (1999): Rough Set Methods for the Synthesis and Analysis of Concurrent Processes, ICS PAS Reports 893 , Institute of Computer Science of the Polish Academy of Sciences, pp. 1-66.

G. SOFTWARE SYSTEMS

  1. The Rough Set Exploration System http://logic.mimuw.edu.pl/~rses
  2. The Rosetta WWW homepage. Available at http://rosetta.lcb.uu.se/