Skip to main content

Rough Sets and their Applications

  • Conference paper
Computational Intelligence in Theory and Practice

Part of the book series: Advances in Soft Computing ((AINSC,volume 8))

Abstract

The paper discusses basic concepts of rough set theory. Starting point of the theory are data tables which are used to define rudiments of the theory: approximations, dependency and reduction of attributes, decision rules and others. Various applications of the theory are outlined and future problems pointed out.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Albaraan, M.: Weak controllability in a parallel flow model of computation and its relationship with rough sets. In: P. P. Wang (ed.), Proceedings of the International Workshop on Rough Sets and Soft Computing at Second Annual Joint Conference on Information Sciences (JCIS’95), Wrightsville Beach, North Carolina, 28 September — 1 October (1995) 205–20

    Google Scholar 

  2. An, A., Chan, C., Shan, N., Cercone, N., Ziarko, W.: Applying knowledge discovery to predict water-supply consumption. IEEE Expert 12/4 (1997) 72–78

    Article  Google Scholar 

  3. Baltzersen, J. K.: An attempt to predict stock market data: A rough sets approach. Master Thesis, supervisor J. Komorowski. Knowledge Systems Group, The Norwegian University of Science and Technology, Trondheim, Norway (1995)

    Google Scholar 

  4. Beaubouef, T., Petry, F. E., Buckles, B. P.: Extension of the relational database and its algebra with rough set techniques. In: W. Ziarko (ed.), Computational Intelligence: An International Journal 11/2 (1995) (special issue) 233–245

    Google Scholar 

  5. Cattaneo, G.: Mathematical foundations of roughness and fuzziness. In: S. Tsumoto at al, (eds.), The fourth International Workshop on Rough Sets, Fuzzy Sets and Machine Discovery, Proceedings, The University of Tokyo (1996) 241–247

    Google Scholar 

  6. Chakraborty, M. K., Banerjee, M.: In search of a common foundation for rough sets and fuzzy sets. In: Proceedings of the Fifth European Congress on Intelligent Techniques and Soft Computing (EUFIT’97), Aachen, Germany, Verlag Mainz (1997) 1218–220

    Google Scholar 

  7. Chen, R., Lin, T.Y.: Supporting rough set theory in very large databases using Oracle RDBMS. In: Y.-Y. Chen, K. Hirota, and J.-Y. Yen (eds.), Proceedings of Asian Fuzzy Systems Symposium — Soft Computing in Intelligent Systems and Information Processing, December 11–14, Kenting, Taiwan, ROC. (1996) 332–337

    Google Scholar 

  8. Czogala, E., Mrozek, A., Pawlak, Z.: The idea of rough-fuzzy controller. International Journal of Fuzzy Sets and Systems 72 (1995) 61–63

    Article  Google Scholar 

  9. Czyzewski, A.: New learning algorithms for the processing of old audio recordings. In: 99th Convention of the Audio Engineering Society, October 69, New York, USA, preprint 4078 (1995)

    Google Scholar 

  10. Czyzewski, A.: Speaker—independent recognition of digits — Experiments with neural networks, fuzzy logic and rough sets. In: T. Y. Lin (ed.), Journal of the Intelligent Automation and Soft Computing 2/2 (1996) 133–146

    Google Scholar 

  11. Czyzewski, A., Kostek, B.: Rough set-based filtration of sound applicable to hearing prostheses. In: 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) 168–175

    Google Scholar 

  12. Czyzewski, A., Kostek, B.: Restoration of old records employing artificial intelligence methods. Proceedings of LASTED International Conference — Artificial Intelligence, Expert Systems and Neural Networks, August 19–21, Honolulu, Hawaii, USA (1996) 372–375

    Google Scholar 

  13. Deogun, J., Raghavan, V., Sarkar, A., Sever, H.: Data mining: Trends in research and development. In: T. Y. Lin, N. Cercone (eds.), Rough Sets and Data Mining. Analysis of Imprecise Data. Kluwer Academic Publishers, Boston, Dordrecht (1997) 9–45

    Chapter  Google Scholar 

  14. Dubois, D., Parade, H.: Putting rough sets and fuzzy sets together. In: R. Slowinski (ed.), Intelligent Decision Support — Handbook of Advances and Applications of the Rough Set Theory. Kluwer Academic Publishers, Dordrecht, Boston, London (1992) 203–232

    Google Scholar 

  15. Eiben, A. E., Euverman, T. J., Kowalczyk, W., Slisser, F.: Modelling customer retention with statistical techniques, rough data models, and genetic programming. In: S. K. Pal, A. Skowron (eds.), Fuzzy Sets, Rough Sets, and Decision Making Processes. Springer-Verlag, Singapore (in print)

    Google Scholar 

  16. Fernandes—Baizan, M. C., Menasalvas Ruiz, E., Pena, J. M., Santos, E.: Using RDMS to mine microbiological data. In: Nagib C. Callaos (ed.), Proceedings of the International Conference on Information Systems Analysis and Synthesis (ISAS’96), July 22–26, Orlando, USA (1996) 551–554

    Google Scholar 

  17. Frege, G.: Grundgezetze der Arithemtik: Begrieffschriftliche abgeleitet. Vol. 1. Jena (1893), Vol. 2, Jena (1903)

    Google Scholar 

  18. Garcia, A., Shasa, D.: Using rough sets to order questions leading to database queries. In: Nagib C. Callaos (ed.), Proceedings of the International Conference on Information Systems Analysis and Synthesis (ISAS’96), July 22–26, Orlando, USA (1996) 555–560

    Google Scholar 

  19. Golan, R., Ziarko, W.: A methodology for stock market analysis utilizing rough set theory. In: Proceedings of IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, New York City (1995) 32–40

    Google Scholar 

  20. Greco, S., Matarazzo, B., Slowinski, R.: A new rough set approach to evaluation of bankruptcy risk. In: C. Zopounidis (ed.), New Operational Tools in the Management of Financial Risks, Kluwer Academic Publishers, Dordrecht (to appear)

    Google Scholar 

  21. Grzymala-Busse, J. W.: Knowledge acquisition under uncertainty — a rough set approach. Journal of Intelligent and Robotics Systems 1 (1988) 3–16

    Article  MathSciNet  Google Scholar 

  22. Grzymala-Busse, J. W., Goodwin, L. K.: Predicting preterm birth risk using machine learning from data with missing values. In: S. Tsumoto (ed.), Bulletin of International Rough Set Society 1/1 17–21

    Google Scholar 

  23. Grzymala-Busse, J. W., Gunn, J. D.: Global temperature analysis based on the rule induction system LERS. In: Proceedings of the Fourth International Workshop on Intelligent Information Systems, Augustow, Poland, June 5–9, 1995, Institute of Computer Science, Polish Academy of Sciences, Warsaw (1995) 148–158

    Google Scholar 

  24. Grzymala-Busse, J. W., Sedelow, S. Y., Sedelow, W. A. Jr.: Machine learning & knowledge acquisition, rough sets, and the English semantic code. In: T. Y. Lin (ed.), Proceedings of the Workshop on Rough Sets and Data Mining at 23rd Annual Computer Science Conference. Nashville, Tennessee, March 2 (1995) 86–104

    Google Scholar 

  25. T. Y. Lin, N. Cercone (eds.), Rough Sets and Data Mining. Analysis of Imprecise Data. Kluwer Academic Publishers, Boston, Dordrecht (1997) 91–107

    Google Scholar 

  26. Gunn, J. D., Grzymala-Busse, J. W.: Global temperature stability by rule induction: An interdisciplinary bridge. Human Ecology 22 (1994) 59–81

    Article  Google Scholar 

  27. Hadjimichael, M., Wasilewska, A.: Rough sets-based study of voter preference in 1988 USA presidential election. In: R. Slowinski (ed.), Intelligent Decision Support—Handbook of Applications and Advances of the Rough Sets Theory. Kluwer Academic Publishers, Dordrecht (1992) 137–152

    Google Scholar 

  28. Ho, T. B., Funakoshi, K.: Information retrieval using rough sets (submitted to Journal of Japanese Society for Artificial Intelligence)

    Google Scholar 

  29. Jackson, A. G., Leclair, S. R., Ohmer, M. C., Ziarko, W., Al-Kamhawi, H.: Rough sets applied to material data. Acta Metallurgica et Materialia (1996) 44–75

    Google Scholar 

  30. Kostek, B., Szczerba, M.: Rough set—based analysis of musical databases. In: Proceedings of the Fourth European Congress on Intelligent Techniques and Soft Computing, (EUFIT’96), September 2–5, Aachen, Germany, Aachen, (1996) 1 144–148

    Google Scholar 

  31. Krysinski, J.: Application of the rough sets theory to the analysis of structure—activity relationships of antimicrobial pyridinium compounds. Die Pharmazie 50 (1995) 593–597

    Google Scholar 

  32. Krysinski, J.: Rough sets in the analysis of the structure—activity relationships of antifungal imidazolium compounds. Journal of Pharmaceutical Sciences 84/2 (1995) 243–247

    Google Scholar 

  33. Krusinska, E., Slowinski, R., Stefanowski, J.: Discriminant versus rough set approach to vague data analysis. Journal of Applied Statistics and Data Analysis 8 (1992) 43–56

    MATH  Google Scholar 

  34. Lin, T. Y.: Fuzzy reasoning and rough sets. In: W. Ziarko (ed.), Rough Sets, Fuzzy Sets and Knowledge Discovery (RSKD’93). Workshops in Computing, Springer-Verlag & British Computer Society, London, Berlin (1994) 343–348

    Chapter  Google Scholar 

  35. T. Y. Lin (ed.): Proceedings of the Third International Workshop on Rough Sets and Soft Computing (RSSC’94). San Jose State University, San Jose, California, USA, November 10–12 (1994)

    Google Scholar 

  36. T. Y. Lin (ed.): Proceedings of the Workshop on Rough Sets and Data Mining at 23rd Annual Computer Science Conference. Nashville, Tennessee, March 2 (1995)

    Google Scholar 

  37. Lin, T. Y.: Rough-fuzzy controllers for complex systems. In: P. P. Wang (ed.), Proceedings of the International Workshop on Rough Sets and Soft Computing at Second Annual Joint Conference on Information Sciences (JCIS’95), Wrightsville Beach, North Carolina, 28 September — 1 October (1995) 18–21

    Google Scholar 

  38. Lin, T. Y.: Neighborhood systems — A qualitative theory for fuzzy and rough sets. In: P. P. Wang (ed.), Proceedings of the International Workshop on Rough Sets and Soft Computing at Second Annual Joint Conference on Information Sciences (JCIS’95), Wrightsville Beach, North Carolina, 28 September — 1 October (1995) 255–258

    Google Scholar 

  39. Lin, T. Y.: Fuzzy controllers: An integrated approach based on fuzzy logic, rough sets, and evolutionary computing. In: T. Y. Lin (ed.), Proc. of the Workshop on Rough Sets and Data Mining at 23-rd Annual Computer Science Conference, Nashville, Tennessee, 2 March (1995)

    Google Scholar 

  40. T. Y. Lin (ed.): CSC’95, 23rd Annual Computer Science Conference on Rough Sets and Database Mining, Conference Proceedings, March 2, San Jose State University, San Jose, California, USA (1995)

    Google Scholar 

  41. Lin, T. Y.: Fuzzy controllers: An integrated approach based on fuzzy logic, rough sets, and evolutionary computing. In: T. Y. Lin and N. Cecerone (eds.), Rough Sets and Data Mining. Analysis for Imprecise Data, Kluwer Academic Publishers, Dordrech (1997) 123–138

    Chapter  Google Scholar 

  42. T. Y. Lin, N. Cercone (eds.): Rough Sets and Data Mining. Analysis of Imprecise Data. Kluwer Academic Publishers, Boston, Dordrecht (1997)

    MATH  Google Scholar 

  43. T. Y. Lin, A. M. Wilderberg (eds.): Soft Computing, Proceedings of the Third International Workshop on Rough Sets and Soft Computing (RSSC’94), November 10–12, San Jose State University, San Jose, California, USA (1994)

    Google Scholar 

  44. Lin, T. Y., Wildberger, M.: Algebra and geometry of rough logic controllers 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, Proceedings (RS96FD), November 6–8, The University of Tokyo (1996) 111–117

    Google Scholar 

  45. Lingras, P.: Rough neural networks. In: Proceedings of the Sixth International Conference, Information Processing and Management of Uncertainty in Knowledge—Based Systems (IPMU’96), July 1–5, Granada, Spain (1996) 2 1445–1450

    Google Scholar 

  46. Lingras, P.: Learning using rough Kohonen neural networks classifiers. In: P. Borne, G. Dauphin-Tanguy, C. Sueur, and S. El Khattabi (eds.), Proceedings of IMACS Multiconference: Computational Engineering in Systems Applications (CESA’96) July 9–12, Lille, France, Gerf EC Lille — Cite Scientifique (1996) 3/4 753–757

    Google Scholar 

  47. Mitra, S., Banerjee, M.: Knowledge-based neural net with rough sets. In: T. Yamakawa et al. (eds.), Methodologies for the Conception, Design, and Application of Intelligent Systems, Proceedings of the Fourth International Conference on Soft Computing (IIZUKA’96), Iizuka, Japan 1996, World Scientific (1996) 213–216

    Google Scholar 

  48. Moradi, H., Grzymala-Busse, J. W., Roberts, J.: Entropy of English text: Experiments with humans and machine learning system based on rough sets. In: P. P. Wang (ed.), Proceedings of the International Workshop on Rough Sets and Soft Computing at Second Annual Joint Conference on Information Sciences (JCIS’95), Wrightsville Beach, North Carolina, 28 September — 1 October (1995) 87–88

    Google Scholar 

  49. Mrozek, A.: Rough sets in computer implementation of rule-based control of industrial processes. In: R. Slowinski (ed.), Intelligent Decision Support — Handbook of Applications and Advances of the Rough Sets Theory. Kluwer Academic Publishers, Dordrecht (1992) 19–31

    Google Scholar 

  50. Munakata, T.: Rough control: A perspective. In: 23rd Annual Computer Science Conference on Rough Sets and Database Mining (CSC’95), Conference Proceedings, March 2, San Jose University, San Jose, California, USA (1995)

    Google Scholar 

  51. Munakata, T., Pawlak, Z.: Rough control application of rough set theory to control. Fourth European Congress on Intelligent Techniques and Soft Computing, Proceedings EUFIT’96), Volume I, September 2–5, Germany (1996) 209–218

    Google Scholar 

  52. Nakamura, A.: Fuzzy quantifiers and rough quantifiers. In: P. P. Wang (ed.), Advances in Fuzzy Theory and Technology II (1994) 111–131 Nguyen, H. Son, Szczuka, M., Slezak, D.: Neural network design: Rough set approach to real—valued data. In: J. Komorowski, J. Zytkow, (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 (1997) 359–366

    Google Scholar 

  53. Nguyen, T., Swiniarski, R., Skowron, A., Bazan, J., Thagarajan, K.: Applications of rough sets, neural networks and maximum likelihood for texture classification based on singular value decomposition. In: T. Y. Lin (ed.), The Third International Workshop on Rough Sets and Soft Computing Proceedings (RSSC’94), November 10–12, San Jose State University, San Jose, California, USA, 332–339

    Google Scholar 

  54. T. Y. Lin and A. M. Wildberger (eds.), Soft Computing Councils, Inc., San Diego (1995) 157–160

    Google Scholar 

  55. Nowicki, R., Slowinski, R., Stefanowski, J.: Rough sets analysis of diagnostic capacity of vibroacoustic symptoms. Journal of Computers and Mathematics with Applications 24 (1992) 109–123

    Article  MATH  Google Scholar 

  56. Nurmi, H., Kacprzyk, J., Fedrizzi, M.: Theory and methodology: Probabilistic, fuzzy and rough concepts in social choice. European Journal of Operational Research, Elsevier (1996) 264–277

    Google Scholar 

  57. E. Orlowska (ed.): Incomplete Information: Rough Set Analysis. PhysicaVerlag, Heidelberg (1997)

    Google Scholar 

  58. Ohm, A., Vinterbo, S., Szymanski, P., Komorowski, J.: Modelling cardiac patient set residuals using rough sets. In: Proceedings of AMIA Annual Fall Symposium (formerly SCAMC), Nashville, TN, USA, October 25–29 (1997) 203–207

    Google Scholar 

  59. Technical Report, Knowledge Systems Group, Norwegian University of Science and Technology, Trondheim, Norway (1997) (extended version)

    Google Scholar 

  60. S. K. Pal, A. Skowron (eds.): Fuzzy Sets, Rough Sets and Decision Making Processes. Springer—Verlag, Singapore (in print)

    Google Scholar 

  61. Paszek, P., Wakulicz-Deja, A.: Optimization diagnose in progressive encephalopathy applying the rough set theory. In: Proceedings of the Fourth European Congress on Intelligent Techniques and Soft Computing (EUFIT’96), September 2–5, Aachen, Germany, Aachen, (1996) 1 192–196

    Google Scholar 

  62. Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11 (1982) 341–356

    Article  MathSciNet  MATH  Google Scholar 

  63. Pawlak, Z.: Rough sets and fuzzy sets. J. of Fuzzy Sets and Systems 17 (1985) 99–102

    Article  MathSciNet  MATH  Google Scholar 

  64. Pawlak, Z.: Rough sets and fuzzy sets. In: C. Jinshong (ed.), Proceedings of ACM, Computer Science Conference, February 28 — March 2, Nashville, Tennessee (1995) 262–264

    Google Scholar 

  65. Pawlak, Z.: Rough Sets, Theoretical Aspects of Reasoning above Data. Kluwer Academic Publishers, Dordrecht, Boston, London (1991)

    Google Scholar 

  66. Pawlak, Z., Skowron, A.: Rough membership functions. In: R. R. Yeager, M. Fedrizzi, J Kacprzyk (eds.), Advances in the Dempster-Shafer Theory of Evidence, John Wiley and Sons, New York (1994) 251–271

    Google Scholar 

  67. Pawlak, Z., Slowinski, R.: Decision analysis using rough sets. International Transactions on Operational Research 1 (1994) 107–104

    Article  MATH  Google Scholar 

  68. Pawlak, Z., Grzymala-Busse, J., Slowinski, R. Ziarko, W.: Rough sets. Communication of the ACM 38 (1995) 88–95

    Article  Google Scholar 

  69. Peters III, J. F., Ramanna, S.: A rough set approach to assessing software quality: Concepts and rough Petri net models. In: S. K. Pal, A. Skowron (eds.), Fuzzy Sets, Rough Sets and Decision Making Processes. Springer—Verlag, Singapore (in preparation)

    Google Scholar 

  70. Peterson, G. I.: Rough classification of pneumonia patients using a clinical data—base. In: W. Ziarko (ed.), Rough Sets, Fuzzy Sets and Knowledge Discovery (RSKD’93). Workshops in Computing, Springer—Verlag & British Computer Society, London, Berlin (1994) 412–419

    Chapter  Google Scholar 

  71. Plonka, L., Mrozek, A.: Rule-based stabilization of the inverted pendulum. Computational Intelligence: An International Journal 11 (1995) 348–356

    Google Scholar 

  72. Polkowski, L.: Mathematical morphology of rough sets. Bull. Polish Acad. Sci. Math. 41 /3 (1993) 241–273

    MathSciNet  MATH  Google Scholar 

  73. Polkowski, L, Skowron, A.: Rough mereology: A new paradigm for approximate reasoning. Journal of Approximate Reasoning 15/4 (1996) 333–365

    Article  MathSciNet  MATH  Google Scholar 

  74. Quafafou, M.: Towards a transition from the crisp rough set theory to a fuzzy one. In: Proceedings of the Poster Session of the Ninth International Symposium on Methodologies for Intelligent Systems (ISMIS’96), Zakopane, Poland, June 9–13, Oak Ridge Laboratory (1996) 67–80

    Google Scholar 

  75. Ruhe G.: Qualitative analysis of software engineering data using rough sets. 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) 292–299

    Google Scholar 

  76. Shenoi, S.: Rough sets in fuzzy databases. In: P. P. Wang (ed.), Proceedings of the International Workshop on Rough Sets and Soft Computing at Second Annual Joint Conference on Information Sciences (JCIS’95), Wrightsville Beach, North Carolina, 28 September —1 October (1995) 263–264

    Google Scholar 

  77. Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: R. Slowinski (ed.), Intelligent Decision Support — Handbook of Advances and Applications of the Rough Set Theory. Kluwer Academic Publishers, Dordrecht, Boston, London (1992) 311–369

    Google Scholar 

  78. Skowron, A., Grzymala-Busse, J.: From rough set theory to evidence theory. In: R. R. Yeager, M. Fedrizzi, J. Kacprzyk (eds.) Advances in the Dempster-Shafer Theory of Evidence, John Wiley and Sons, New York (1994) 193–236

    Google Scholar 

  79. Slowinski, K., Sharif, E. S.: Rough sets approach to analysis of data of diagnostic peritoneal lavage applied for multiple injuries patients. In: W. Ziarko (ed.), Rough Sets, Fuzzy Sets and Knowledge Discovery (RSKD’93). Workshops in Computing, Springer—Verlag & British Computer Society, London, Berlin (1994) 420–425

    Google Scholar 

  80. Slowinski, K., Slowinski, R., Stefanowski, J.: Rough sets approach to analysis of data from peritoneal lavage in acute pancreatitis. Medical Informatics 13/3 (1988) 143–159

    Google Scholar 

  81. Slowinski, K., Stefanowski, J., Antczak, A., Kwas, Z.: Rough sets approach to the verification of indications for treatment of urinary stones by extracorporeal shock wave lithotripsy (ESWL). In: T.Y. Lin, A. M. Wildberger (eds.), Soft Computing: Rough Sets, Fuzzy Logic, Neural Networks, Uncertainty Management, Knowledge Discovery. Simulation Councils, Inc., San Diego, CA (1995) 93–96

    Google Scholar 

  82. R. Slowinski (ed.): Intelligent Decision Support — Handbook of Advances and Applications of the Rough Set Theory. Kluwer Academic Publishers, Dordrecht, Boston, London (1992)

    Google Scholar 

  83. Slowinski, R.: Rough set approach to decision analysis. AI Expert 10 (1995) 18–25

    Google Scholar 

  84. Slowinski, R., Zopounidis, C.: Applications of the rough set approach to evaluation of bankruptcy risk. Working Paper 93–08, Decision Support System Laboratory, Technical University of Crete, June (1993)

    Google Scholar 

  85. Slowinski, R., Zopounidis, C.: International J. Intelligent Systems in Accounting, Finance & Management 4/1 (1995) 27–41

    Google Scholar 

  86. Slowinski, R., Zopounidis, C.: Rough set sorting of firms according to bankruptcy risk. In: M. Paruccini (ed.), Applying Multiple Criteria Aid for Decision to Environmental Management, Kluwer, Dordrecht, Netherlands (1994) 339–357

    Google Scholar 

  87. Slowinski, R., Zopounidis, C., Dimitras, A.I.: Prediction of company acquisition in Greece by means of the rough set approach. European Journal of Operational Research 100 (1997) 1–15

    Article  MATH  Google Scholar 

  88. Srinivasan, P.: The importance of rough approximations for information retrieval. Journal of Man—Machine Studies 34 (1991) 657–671

    Article  Google Scholar 

  89. Swiniarski, R., Hunt, F., Chalvet, D., Pearson, D.: Feature selection using rough sets and hidden layer expansion for rupture prediction in a highly automated production system. In: Proceedings of the 12th International Conference on Systems Science, September 12–15, Wroclaw, Poland (1995); see also: Systems Science 23/1 (1997)

    Google Scholar 

  90. Swiniarski, R., Hunt, F., Chalvet, D., Pearson, D.: Intelligent data processing and dynamic process discovery using rough sets, statistical reasoning and neural networks in a highly automated production systems. In: Proceedings

    Google Scholar 

  91. of the First European Conference on Application of Neural Networks in Industry, August, Helsinki, Finland (1995)

    Google Scholar 

  92. Szczuka, M.: Rough set methods for constructing neural network. In: Proceedings of the Third Biennal Joint Conference On Engineering Systems Design and Analysis, Session on Expert Systems, Montpellier, France (1996) 9–14

    Google Scholar 

  93. Szladow, A., Ziarko, W.: Rough sets: Working with imperfect data. AI Expert 8 (1993) 36–41

    Google Scholar 

  94. Szladow, A., Ziarko W.: Adaptive process control using rough sets. Proceedings of the International Conference of Instrument Society of America, ISA/93, Chicago (1993) 1421–1430

    Google Scholar 

  95. Szladow, A., Ziarko W.: Application of rough sets theory to process control. Proceedings of Calgary 93 Symposium of Instrument Society of America, Calgary (1993)

    Google Scholar 

  96. Tanaka, H., Ishibuchi, H., Shigenaga, T.: Fuzzy inference system based on rough sets and its application to medical diagnostic. In: R. Slowinski (ed.), Intelligent Decision Support — Handbook of Applications and Advances of the Rough Sets Theory. Kluwer Academic Publishers, Dordrecht (1992) 111–117

    Google Scholar 

  97. Tanaka, H., Tsumoto, S.: Incremental learning of probabilistic rules from clinical databases based on rough set theory. In: P. P. Wang (ed.), 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 (1997) 387–390

    Google Scholar 

  98. Teghem, J., Benjelloun, M.: Some experiments to compare rough set theory and ordinal statistical methods. In: R. Slowinski (ed.), Intelligent Decision Support — Handbook of Applications and Advances of the Rough Sets Theory. Kluwer Academic Publishers, Dordrecht (1992) 267–286

    Google Scholar 

  99. Thiele, H.: Fuzzy rough sets versus rough fuzzy sets — An interpretation and a comparative study using concepts of modal logics. In: Proceedings of the Fifth European Congress on Intelligent Techniques and Soft Computing (EUFIT’97), September 9–11, Aachen, Germany, Verlag Mainz (1997) 159–167

    Google Scholar 

  100. Tseng, H. Ch., Lin, T. Y., Chi, C. W.: Adaptive aggregation of modular control. In: Y.-Y. Chen, K. Hirota, and J.-Y. Yen (eds.), Proceedings of 1996 ASIAN FUZZY SYSTEMS SYMPOSIUM — Soft Computing in Intelligent Systems and Information Processing, December 11–14, Kenting, Taiwan, ROC. (1996) 506–508

    Chapter  Google Scholar 

  101. Tsumoto, S.: Domain experts’ interpretation of rules induced from clinical databases. In: Proceedings of the Fifth European Congress on Intelligent Techniques and Soft Computing (EUFIT-97), Aachen, Germany, Aachen, (1997) 1 1639–1642

    Google Scholar 

  102. Tsumoto, S.: Extraction of expert’s decision process from clinical databases using rough set model. In: J. Komorowski, J. Zytkow, (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 (1997) 58–67

    Chapter  Google Scholar 

  103. S. Tsumoto, S. Kobayashi, T. Yokomori, H. Tanaka, 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)

    Google Scholar 

  104. Tsumoto, S., Tanaka, H.: Automated discovery of functional components of proteins from amino-acid sequences based on rough sets and change of representation. In: U. M. Fayyad, R. Uthurusamy (eds.), Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD’95), August 20–21, 1995, Montreal, AAAI Press, Menlo Park CA (1995) 318–324

    Google Scholar 

  105. Tsumoto, S., Ziarko, W.: The application of rough sets—based data mining technique to differential diagnosis of meningoencephalitis. In: Z. W. Ras, M. Michalewicz (eds.), Proceedings of the Ninth International Symposium on Methodologies for Intelligent Systems. Zakopane, Poland, June 9–13, Lecture Notes in Artificial Intelligence (ISMIS’96) 1079, Springer—Verlag, Berlin (1996) 438–447

    Google Scholar 

  106. Wakulicz—Deja, A., Paszek, P.: Diagnose progressive encephalopathy applying the rough set theory. International Journal of Medical Informatics 46 (1997) 119–127

    Article  Google Scholar 

  107. Wakulicz-Deja, A., Paszek, P., Marszal-Paszek, B., Emrich, E.: Applying rough sets to diagnosis in children’s neurology. In: Proceedings of the Sixth International Conference, Information Processing and Management of Uncertainty in Knowledge—Based Systems (IPMU’96), July 1–5, Granada, Spain (1996) 3 1463–1468

    Google Scholar 

  108. P. P. Wang (ed.): Proceedings of the International Workshop on Rough Sets and Soft Computing at Second Annual Joint Conference on Information Sciences (JCIS’-95), Wrightsville Beach, North Carolina, 28 September — 1 October (1995)

    Google Scholar 

  109. P. P. Wang (ed.): 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 (1997)

    Google Scholar 

  110. Woolery, L., Van Dyne, M., Grzymala-Busse, J. W., Tsatsoulis, C.: Machine learning for development of an expert system to support nurses’ assessment of preterm birth risk. In: Nursing Informatics: An International Overview for Nursing in a Technological Era, Proceedings of the Fifth International Conference on Nursing Use of Computers and Information Sci., June 17–22, San Antonio, TX, Elsevier (1994) 357–361

    Google Scholar 

  111. Yao, Y. Y.: On combining rough and fuzzy sets. In: T. Y. Lin (ed.), Proceedings of the Workshop on Rough Sets and Data Mining at 23rd Annual Computer Science Conference. Nashville, Tennessee, March 2 (1995) 165–172

    Google Scholar 

  112. Yao, Y. Y.: Combination of rough and fuzzy sets based on alpha-level sets. In: T. Y. Lin, N. Cercone (eds.), Rough Sets and Data Mining. Analysis of Imprecise Data. Kluwer Academic Publishers, Boston, Dordrecht (1997) 301–321

    Google Scholar 

  113. Yao, Y. Y., Wong, S. K. M.: Generalization of rough sets using relationships between attribute values. In: Proceedings of the Second Annual Joint Conference on Information Sciences, Wrightsville Beach, N.C. USA, September 28 — October 1 (1995) 245–253

    Google Scholar 

  114. Yasdi, R.: Combining rough sets learning and neural learning method to deal with uncertain and imprecise information. Neurocomputing 7 (1995) 61–84

    Article  MATH  Google Scholar 

  115. Zadeh, L.: Fuzzy graphs, rough sets and information granularity. In: Proc. Third Int. Workshop on Rough Sets and Soft Computing, November 10–12, San Jose (1994)

    Google Scholar 

  116. Zadeh, L.: Information granulation, fuzzy logic and rough sets. In: 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)

    Google Scholar 

  117. Zhang, Q., Han, Z., Wen, F.: A new approach for fault diagnosis in power systems based on rough set theory. In: Proceedings of International Conference on Advances in Power System Control, Operation and Management (APSCOM’97), Hong Kong, China, November 11–14, (1997)

    Google Scholar 

  118. Ziarko, W.: Acquisition of control algorithms from operation data. In: R. Slowinski (ed.), Intelligent Decision Support, Handbook of Applications and Advances of the Rough Set Theory, Kluwer Academic Publishers, Boston, London, Dordrecht (1992) 61–75

    Google Scholar 

  119. Ziarko, W.: Generation of control algorithms for computerized controllers by operator supervised training. Proceedings of the Eleventh TASTED International Conference on Modeling, Identification and Control, Innsbruck, Austria (1992) 510–513

    Google Scholar 

  120. W. Ziarko (ed.): Rough Sets, Fuzzy Sets and Knowledge Discovery. Proceeding of the International Workshop on Rough Sets and Knowledge Discovery (RSKD’93), Banff, Alberta, Canada, Springer Verlag, Berlin, Heidelberg, New York, London, Paris, Tokyo, Hong Kong, Barcelona, Budapest (1993)

    Google Scholar 

  121. W. Ziarko (ed.): Rough Sets, Fuzzy Sets and Knowledge Discovery (RSKD’93). Workshops in Computing, Springer—Verlag & British Computer Society, London, Berlin (1994)

    Google Scholar 

  122. W. Ziarko (ed.): Computational Intelligence: An International Journal 11/2 (1995) (special issue)

    Google Scholar 

  123. Ziarko, W., Katzberg, J.: Control algorithms acquisition, analysis and reduction: machine learning approach. In: Knowledge-Based Systems Diagnosis, Supervision and Control, Plenum Press, Oxford (1989) 167–178

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pawlak, Z. (2001). Rough Sets and their Applications. In: Reusch, B., Temme, KH. (eds) Computational Intelligence in Theory and Practice. Advances in Soft Computing, vol 8. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1831-4_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-7908-1831-4_5

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-1357-9

  • Online ISBN: 978-3-7908-1831-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics