Skip to main content

Soft Computing Pattern Recognition, Data Mining and Web Intelligence

  • Chapter
Intelligent Technologies for Information Analysis

Abstract

The relevance of fuzzy logic, artificial neural networks, genetic algorithms, and rough sets to pattern recognition problems is described through examples. Different integrations of these soft computing tools are illustrated. The significance of the soft computing approach in data mining, knowledge discovery, and Web mining is discussed. Various existing algorithms and tools in this regard are reviewed. Finally, some research challenges and the scope of future research are outlined.

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 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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. D. Alahakoon, S.K. Halgamuge, B. Srinivasan: Dynamic self organizing maps with controlled growth for knowledge discovery. IEEE Transactions on Neural Networks, 11, 601–614 (2000)

    Article  Google Scholar 

  2. W.H. Au, K.C.C. Chan: An effective algorithm for discovering fuzzy rules in relational databases. In: Proceedings of IEEE International Conference on Fuzzy Systems FUZZ IEEE 98 ( Alaska, May 1998 ) pp. 1314–1319

    Google Scholar 

  3. M. Banerjee, S. Mitra, S.K. Pal: Rough fuzzy MLP: Knowledge encoding and classification. IEEE Transactions on Neural Networks, 9 (6), 1203–1216 (1998)

    Article  Google Scholar 

  4. S. Bengio, Y. Bengio: Taking on the curse of dimensionality in joint distribution using neural networks. IEEE Transactions on Neural Networks, 11, 550–557 (2000)

    Article  Google Scholar 

  5. Y. Bengio, J.M. Buhmann, M. Embrechts, J.M. Zurada: Introduction to the special issue on neural networks for data mining and knowledge discovery. IEEE Transactions on Neural Networks, 11, 545–549 (2000)

    Article  Google Scholar 

  6. J.C. Bezdek, S.K. Pal (eds.): Fuzzy Models for Pattern Recognition: Methods that Search for Structures in Data ( IEEE Press, New York, 1992 )

    Google Scholar 

  7. D. Bikel, R. Schwartz, R. Weischedel: An algorithm that learns what’s in a name. Machine learning, 34 (Special issue on Natural Language Learning)(1/3), 211–231 (1999)

    Google Scholar 

  8. P. Bosc, O. Pivert, L. Ughetto: Database mining for the discovery of extended functional dependencies. In: Proceedings of NAFIPS 99 ( New York, USA, June 1999 ) pp. 580–584

    Google Scholar 

  9. M. Boughanem, C. Chrisment, J. Mothe, C.S. Dupuy, L. Tamine; Connection-ist and genetic approaches for information retrieval. In: F. Crestani, G. Pasi (eds.), Soft Computing in Information Retrieval: Techniques and Applications ( Physica Verlag, Heidelberg, 2000 ) 50, pp. 102–121.

    Google Scholar 

  10. M. Boughanem, T. Dkaki, J. Mothe, C. Soule-Dupuy: Mercure at trec7. In: Proceedings of the 7th International Conference on Text Retrieval, TREC7 ( Gaithrsburg, MD, 1998 )

    Google Scholar 

  11. S. Brin, L. Page: The anatomy of a large scale hypertextual web search engine. In: Proceedings of Eighth International WWW Conference ( Brisbane, Australia, April 1998 ) pp. 107–117

    Google Scholar 

  12. H. Chen, M. Ramsay, P. Li: The Java search agent workshop. In: F.Crestani, G.Pasi (eds.), Soft Computing in Information Retrieval: Techniques and Applications ( Physica Verlag, Heidelberg, 2000 ) 50, pp. 122–140

    Google Scholar 

  13. D.A. Chiang, L.R. Chow, Y.F. Wang: Mining time series data by a fuzzy linguistic summary system. Fuzzy Sets and Systems, 112, 419–432 (2000)

    Article  MATH  Google Scholar 

  14. V. Ciesielski, G. Palstra: Using a hybrid neural/expert system for database mining in market survey data. In: Proc. Second International Conference on Knowledge Discovery and Data Mining (KDD-96) (Portland, OR, August 2–4, 1996 AAAI Press) pp. 38

    Google Scholar 

  15. K.J. Cios, W. Pedrycz, R. Swiniarski: Data Mining Methods for Knowledge Discovery ( Kluwer, Dordrecht, 1998 )

    Book  MATH  Google Scholar 

  16. F. Crestani, G. Pasi, (eds.): Soft Computing in Information Retrieval: Techniques and Application ( Physica-Verlag, Heidelberg, 2000 )

    Google Scholar 

  17. C. Drummond, D. Ionescu, R. Holte: A learning agent that assists the browsing of software libraries. Technical Report TR-95-12 (University of Ottawa, 1995 )

    Google Scholar 

  18. R.O. Duda, P.E. Hart: Pattern Classification and Scene Analysis ( John Wiley, New York, 1973 )

    MATH  Google Scholar 

  19. O. Etzioni: The World Wide Web: Quagmire or gold mine. Communications of the ACM, 39 (11), 65–68 (1996)

    Article  Google Scholar 

  20. O. Etzioni, M. Perkowitz: Adaptive web sites: An AI challenge. In: Proceedings of Fifteenth National Conference on Artificial Intelligence ( Madison, Wisconsin, July 1998 )

    Google Scholar 

  21. O. Etzioni, O. Zamir• Web document clustering: A feasibility demonstration. In: Proceedings of the 21st Annual International ACM SIGIR Conference, 1998 pp. 46–54

    Google Scholar 

  22. U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy (eds.): Advances in Knowledge Discovery and Data Mining ( AAAI/MIT Press, Menlo Park, CA, 1996 )

    Google Scholar 

  23. I.W. Flockhart, N.J. Radcliffe: A genetic algorithm-based approach to data mining. In: The Second International Conference on Knowledge Discovery and Data Mining (KDD-96) (Portland, OR, August 2-4 1996 AAAI Press) pp. 299

    Google Scholar 

  24. D. Freitag, N. Kushmerick: Boosted wrapper induction. In: Proceedings of AAAI, 2000 pp. 577–583

    Google Scholar 

  25. D. Freitag, A. McCallum: Information extraction from HMM’s and shrinkage. In: Proceedings of AAAI-99 Workshop on Machine Learning for Information Extraction ( Orlando, FL, 1999 )

    Google Scholar 

  26. H. Fukuda, E.L.P. Passos, A.M. Pacheco, L.B. Neto, J. Valerio, V.Jr.De Roberto, E.R. Antonio, L. Chigener: Web text mining using a hybrid system. In: Proceedings of the Sixth Brazilian Symposium on Neural Networks, 2000 pp. 131–136

    Google Scholar 

  27. T. Gedeon, L. Koczy: A model of intelligent information retrieval using fuzzy tolerance relations based on hierarchical co-occurrence of words. In: F. Crestani, G. Pasi (ed.), Soft Computing in Information Retrieval: Techniques and Applications, volume50 (Physica Verlag, Heidelberg, 2000 ) pp. 48–74

    Google Scholar 

  28. D.E. Goldberg: Genetic Algorithms in Search, Optimization and Machine Learning ( Addison-Wesley, Reading, MA, 1989 )

    MATH  Google Scholar 

  29. R.C. Gonzalez, P. Wintz: Digital Image Processing ( Addison-Wesley, Reading, MA, 1987 )

    Google Scholar 

  30. M.D. Gordon: Probabilistic and genetic algorithms for document retrieval. Communications of the ACM, 31 (10), 208–218 (1988)

    Article  Google Scholar 

  31. J.W. Grzymala-Busse: LERS-A knowledge discovery system. In: L.Polkowski, A.Skowron (eds.), Rough Sets in Knowledge Discovery 2, Applications, Case Studies and Software Systems ( Physica-Verlag, Heidelberg, 1998 ) pp. 562–565

    Google Scholar 

  32. J.W. Grzymala-Busse, W.J. Grzymala-Busse, L.K. Goodwin: A closest fit approach to missing attribute values in preterm birth data. In: Proceedings of RSFDGrC’99 ( Yamaguchi, Japan, November 1999 ) pp. 405–413

    Google Scholar 

  33. A. Gyenesei: A fuzzy approach for mining quantitative association rules. TUCS technical reports 336, University of turku, Department of Computer Science, Lemminkisenkatul4, Finland, March 2000

    Google Scholar 

  34. J. Hale, S. Shenoi: Analyzing FD inference in relational databases. Data and Knowledge Engineering, 18, 167–183 (1996)

    Article  MATH  Google Scholar 

  35. X. Hu, N. Cercone: Mining knowledge rules from databases: A rough set approach. In Proceedings of the 12th International Conference on Data Engineering (Washington, February 1996 IEEE Computer Society) pp. 96-105

    Google Scholar 

  36. A. Joshi, R. Krishnapuram: Robust fuzzy clustering methods to support web mining. In: Proc Workshop in Data Mining and Knowledge Discovery, SIGMOD, 1998, 15, pp. 1–8

    Google Scholar 

  37. H. Kargupta: The gene expression messy genetic algorithm. In: Proceedings of the IEEE International Conference on Evolutionary Computation ( Nagoya University, Japan, 1996 ) pp. 631–636

    Chapter  Google Scholar 

  38. H. Kargupta, B.H. Park, D. Hershberger, E. Johnson: Collective data mining: A new perspective toward distributed data mining. Advances in Distributed and Parallel Knowledge Discovery (MIT/AAAI Press, 1999 )

    Google Scholar 

  39. S. Kawasaki, N.B. Nguyen, T.B. Ho: Hierarchical document clustering based on tolerance rough set model. In: Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining (KDD-2000) Workshop on Text Mining Boston, MA (August 2000)

    Google Scholar 

  40. R. Kewley, M. Embrechta, C. Breneman: Data strip mining for the virtual design of pharmaceuticals with neural networks. IEEE Transactions on Neural Networks, 11, 668–679 (2000)

    Article  Google Scholar 

  41. S. Kim, B.T. Zhang: Web document retrieval by genetic learning of importance factors for html tags. In: Proceedings of the International Workshop on Text and Web mining (Melbourne, Australia, August 2000) pp. 13-23,

    Google Scholar 

  42. A. Koenig: Interactive visualization and analysis of hierarchical neural projections for data mining. IEEE Transactions on Neural Networks, 11, 615–624 (2000)

    Article  Google Scholar 

  43. T. Kohonen: Self-organising Maps (Springer, Berlin, Germany, second edition, 1997 )

    Book  Google Scholar 

  44. T. Kohonen, S. Kaski, K. Lagus, J. Salojarvi, J. Honkela, V. Paatero, A. Saarela: Self organization of a massive document collection. IEEE Transactions on Neural Networks, 11, 574–585 (2000)

    Article  Google Scholar 

  45. D.H. Kraft, F.E. Petry, B.P. Buckles, T. Sadasivan: The use of genetic programming to build queries for information retrieval. In: Proceedings of the IEEE Symposium on Evolutionary Computation ( Orlando, FL, 1994 )

    Google Scholar 

  46. R. Krishnapuram, A. Joshi, L. Yi: A fuzzy relative of the k-medoids algorithm with application to document and snippet clustering. In: Proceedings of IEEE Intl. Conf. Fuzzy Systems–FUZZIEEE 99, Korea, 1999

    Google Scholar 

  47. D.H. Lee, M.H. Kim: Database summarization using fuzzy ISA hierarchies. IEEE Transactions on Systems Man and Cybernetics. Part B-Cybernetics, 27, 68–78 (1997)

    Article  Google Scholar 

  48. R.S.T. Lee, J.N.K. Liu: Tropical cyclone identification and tracking system using integrated neural oscillatory leastic graph matching and hybrid RBF network track mining techniques. IEEE Transactions on Neural Networks, 11, 680–689 (2000)

    Article  Google Scholar 

  49. J.H. Lim: Visual keywords: from text retrieval to multimedia retrieval. In: F.Crestani, G.Pasi (eds.), Soft Computing in Information Retrieval: Techniques and Applications ( Physica Verlag, Heidelberg, 2000 ), 50, pp. 77–101

    Google Scholar 

  50. R.P. Lippmann: Pattern classification using neural networks. IEEE Communications Magazine, pp. 47–64 (1989)

    Google Scholar 

  51. B. Liu, W. Hsu, L.F. Mun, H.Y. Lee: Finding interesting patterns using user expectation. IEEE Transactions on Knowledge and Data Engineering, 11, 817–832 (1999)

    Article  Google Scholar 

  52. C. Lopes, M. Pacheco, M. Vellasco, E. Passos: Rule-evolver: An evolutionary approach for data mining. In: Proceedings of RSFDGrC’99 ( Yamaguchi, Japan, November 1999 ) pp. 458–462

    Google Scholar 

  53. H.J. Lu, R. Setiono, H. Liu: Effective data mining using neural networks. IEEE Transactions on Knowledge and Data Engineering, 8, 957–961 (1996)

    Article  Google Scholar 

  54. V.U. Maheswari, A. Siromoney, K.M. Mehata: The variable precision rough set model for web usage mining. In: Proceedings of the First Asia-Pacific Con-ference on Web Intelligence (WI-2001) ( Maebashi, Japan, October 2001 )

    Google Scholar 

  55. M.J. Martin-Bautista, M.A. Vila: A survey of genetic feature selection in mining issues. In: Proceedings of the Congress on Evolutionary Computation (CEC 99), 1999 pp. 13–23

    Google Scholar 

  56. L.J. Mazlack: Softly focusing on data. In: Proceedings of NAFIPS 99 ( New York, June 1999 ) pp. 700–704

    Google Scholar 

  57. D. Merkl, A. Rauber: Document classification with unsupervised artificial neural networks. In: F.Crestani, G.Pasi (eds.), Soft Computing in Information Retrieval: Techniques and Applications, volume50, (Physica Verlag, Heidelberg, 2000 ) pp. 102–121

    Google Scholar 

  58. T.M. Mitchell: Machine Learning ( McGraw-Hill, New York, 1997 )

    MATH  Google Scholar 

  59. T.M. Mitchell: Machine learning and data mining. Communications of the ACM, 42 (11), 1999

    Google Scholar 

  60. P. Mitra, S. Mitra, S.K. Pal: Staging of cervical cancer with soft computing. IEEE Trans. Biomedical Engineering, 47 (7), 934–940 (2000)

    Article  Google Scholar 

  61. S. Mitra, R.K. De, S.K. Pal: Knowledge-based fuzzy MLP for classification and rule generation. IEEE Transactions on Neural Networks, 8, 1338–1350 (1997)

    Article  Google Scholar 

  62. S. Mitra, Y. Hayashi: Neuro-fuzzy rule generation: Survey in soft computing framework. IEEE Transactions on Neural Networks, 11, 748–768 (2000)

    Article  Google Scholar 

  63. S. Mitra, S.K. Pal: Fuzzy multi-layer perceptron, inferencing and rule gener-ation. IEEE Transactions on Neural Networks, 6, 51–63 (1995)

    Article  Google Scholar 

  64. S. Mitra, S.K. Pal: Fuzzy self organization, inferencing and rule generation. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 26, 608–620, 1996

    Article  Google Scholar 

  65. S. Mitra, S.K. Pal, P. Mitra: Data mining in soft computing framework: A survey. IEEE Trans. Neural Networks, 13 (1), 3–14 (2002)

    Article  Google Scholar 

  66. B. Mobasher, V. Kumar, E.H. Han: Clustering in a high dimensional space using hypergraph models. Technical Report TR-97-063, University of Minnesota, Minneapolis, 1997.

    Google Scholar 

  67. T. Mollestad, A. Skowron: A rough set framework for data mining of propo-sitional default rules. Lecture Notes in Computer Science 1079, 448–457 (1996)

    Article  Google Scholar 

  68. D. Nauck: Using symbolic data in neuro-fuzzy classification. In Proceedings of NAFIPS 99 ( New York, June 1999 ) pp. 536–540

    Google Scholar 

  69. E. Noda, A.A. Freitas, H.S. Lopes: Discovering interesting prediction rules with a genetic algorithm. In: Proceedings of IEEE Congress on Evolutionary Computation CEC 99 ( Washington DC, July 1999 ) pp. 1322–1329

    Google Scholar 

  70. S.K. Pal, R.K. De, J. Basak: Unsupervised feature evaluation: A neuro-fuzzy approach. IEEE Transactions on Neural Networks, 11, 366–376 (2000)

    Article  Google Scholar 

  71. S.K. Pal, T.S. Dillon, D.S. Yeung: Soft Computing in Case Based Reasoning ( Springer Verlag, London, 2001 )

    Book  MATH  Google Scholar 

  72. S.K. Pal, D. DuttaMajumder: Fuzzy Mathematical Approach to Pattern Recognition (John Wiley, Halsted Press, New York, 1986 )

    Google Scholar 

  73. S.K. Pal, A. Ghosh, M.K. Kundu (eds.): Soft Computing for Image Processing ( Physica Verlag, Heidelberg, 2000 )

    MATH  Google Scholar 

  74. S.K. Pal, P. Mitra: Case generation: A rough fuzzy approach. In: Proc. Intl. Conf. Case Based Reasoning (ICCBR2001) ( Vancouver, Canada, 2001 )

    Google Scholar 

  75. S.K. Pal, S. Mitra: Neuro-fuzzy Pattern Recognition: Methods in Soft Com-puting ( John Wiley, New York, 1999 )

    Google Scholar 

  76. S.K. Pal, S.Mitra, P. Mitra: Rough fuzzy MLP: Modular evolution, rule generation and evaluation. IEEE Trans. Knowledge and Data Engineering, 15 (1), 14–25 (2003)

    Google Scholar 

  77. S.K. Pal, A. Pal (eds.): Pattern Recognition: From classical to modern approaches ( World Scientific, Singapore, 2001 )

    MATH  Google Scholar 

  78. S.K. Pal, W. Pedrycz, A. Skowron, R. Swiniarski (eds.): Spl. issue on roughneuro computing. Neurocomputing, 36(1–4) (2001)

    Google Scholar 

  79. S.K. Pal, A. Skowron. Rough Fuzzy Hybridization: A New Trend in Decision Making ( Springer-Verlag, Singapore, 1999 )

    MATH  Google Scholar 

  80. S.K. Pal, V. Talwar, P. Mitra: Web mining in soft computing framework: Relevance, state of the art and future direction. IEEE Trans. Neural Networks, 13 (5), 1163–1177 (2002)

    Article  Google Scholar 

  81. S.K. Pal, P.P. Wang (eds.): Genetic Algorithms for Pattern Recognition ( CRC Press, Boca Raton, 1996 )

    Google Scholar 

  82. G. Pasi, G. Bordonga: Application of fuzzy set theory to extend boolean information retrieval. In: F.Crestani, G.Pasi, (eds.), Soft Computing in Information Retrieval: Techniques and Applications ( Physica Verlag, Heidelberg, 2000 ) 50, pp. 21–47

    Google Scholar 

  83. Z. Pawlak: Rough Sets, Theoretical Aspects of Reasoning about Data ( Kluwer Academic, Dordrecht, 1991 )

    MATH  Google Scholar 

  84. M. Pazzani, J. Muramatsu, D. Billsus: Syskill and webert:identifying interesting web sites. In: Proceedings of Thirteenth National Conference on AIpp. 54–61 (1996)

    Google Scholar 

  85. W. Pedrycz: Conditional fuzzy c-means. Pattern Recognition Letters, 17, 625–632 (1996)

    Article  Google Scholar 

  86. W. Pedrycz: Fuzzy set technology in knowledge discovery. Fuzzy Sets and Systems, 98, 279–290 (1998)

    Article  Google Scholar 

  87. L. Polkowski, A. Skowron: Rough mereology: A new paradigm for approximate reasoning. International Journal of Approximate Reasoning, 15(4), 333365 (1996)

    Google Scholar 

  88. L. Polkowski, A. Skowron: Rough Sets in Knowledge Discovery 1 and 2 ( Physica-Verlag, Heidelberg, 1998 )

    Google Scholar 

  89. A. Rosenfeld, A.C. Kak: Digital Picture Processing (Volume 1-2. Academic Press, New York, 1982 )

    Google Scholar 

  90. D.E. Rumelhart, J.L. McClelland (eds.): Parallel Distributed Processing: Explorations in the Microstructures of Cognition, volumel (MIT Press, Cambridge, MA, 1986 )

    Google Scholar 

  91. T. Ryu, C.F. Eick: MASSON: discovering commonalties in collection of objects using genetic programming In: Genetic Programming 1996: Proc. First Annual Conference (Stanford University, CA, July 28-31 1996 MIT Press) pp. 200–208

    Google Scholar 

  92. D. Shalvi, N. De Claris: Unsupervised neural network approach to medical data mining techniques. In: Proceedings of IEEE International Joint Conference on Neural Networks ( Alaska, May 1998 ) pp. 171–176

    Google Scholar 

  93. N. Shan, W. Ziarko: Data-based acquisition and incremental modification of classification rules. Computational Intelligence, 11, 357–370 (1995)

    Article  Google Scholar 

  94. J. Shavlik, T. Eliassi: A system for building intelligent agents that learn to retrieve and extract information. International Journal on User Modeling and user adapted interaction, April 2001 ( Spl. issue on User Modeling and Intelligent Agents )

    Google Scholar 

  95. J. Shavlik, G.G. Towell: Knowledge-based artificial neural networks. Artificial Intelligence, 70 (1-2), 119–165 (1994)

    MATH  Google Scholar 

  96. C.K. Shin, S.J. Yu, U.T. Yun, H.K. Kim: A hybrid approach of neural network and memory based learning to data mining. IEEE Transactions on Neural Networks, 11, 637–646 (2000)

    Article  Google Scholar 

  97. A. Skowron: Extracting laws from decision tables–a rough set approach. Computational Intelligence, 11, 371–388 (1995)

    Article  MathSciNet  Google Scholar 

  98. A. Skowron, L. Polkowski (eds.), Rough Sets in Knowledge Discovery ( Physica-Verlag, Heidelberg, 1998 )

    Google Scholar 

  99. S. Soderland: Learning information extraction rules for semi-structured and free text. Machine learning, 34 (Special issue on Natural Language Learning) 233–272 (1999)

    Article  MATH  Google Scholar 

  100. U. Straccia: A framework for the retrieval of multimedia objects based on four-valued fuzzy description logics. In F.Crestani, G.Pasi, (ed.), Soft Computing in Information Retrieval: Techniques and Applications ( Physica Verlag, Heidelberg, 2000 ) 50, pp. 332–357

    Google Scholar 

  101. A. Teller, M. Veloso: Program evolution for data mining. The International Journal of Expert Systems, 8, 216–236 (1995)

    Google Scholar 

  102. A.B. Tickle, R. Andrews, M. Golea, J. Diederich: The truth will come to light: Directions and challenges in extracting the knowledge embedded within trained artificial neural networks. IEEE Transactions on Neural Networks, 9, 1057–1068 (1998)

    Article  Google Scholar 

  103. J.T. Tou, R.C. Gonzalez: Pattern Recognition Principles ( Addison-Wesley, London, 1974 )

    MATH  Google Scholar 

  104. I.B. Turksen: Fuzzy data mining and expert system development. In: Proceedings of IEEE International Conference on Systems, Man, Cybernetics ( San Diego, CA, October 1998 ) pp. 2057–2061

    Google Scholar 

  105. J.Vesanto, E.Alhoniemi: Clustering of the self-organizing map. IEEE Transactions on Neural Networks, 11, 586–600 (2000)

    Article  Google Scholar 

  106. Q.Wei, G.Chen: Mining generalized association rules with fuzzy taxonomic structures. In: Proceedings of NAFIPS 99 ( New York, June 1999 ) pp. 477–481

    Google Scholar 

  107. S.K. Wong, Y.Y. Yao, C.J. Butz: Granular information retrieval. In F.Crestani, G.Pasi (eds.), Soft Computing in Information Retrieval: Techniques and Applications ( Physica Verlag, Heidelberg, 2000 ) 50, pp. 317–331.

    Google Scholar 

  108. R.Yager: A framework for linguistic and hierarchical queries for document retrieval. In: F.Crestani, G.Pasi (eds.), Soft Computing in Information Retrieval: Techniques and Applications ( Physica Verlag, Heidelberg, 2000 ) 50, pp. 3–20

    Google Scholar 

  109. R.R. Yager: On linguistic summaries of data. In: W.Frawley, G.P. Shapiro (eds.), Knowledge Discovery in Databases ( AAAI/MIT Press, Menlo Park, CA, 1991 ) pp. 347–363

    Google Scholar 

  110. J.J. Yang, R.Korfhage: Query modification using genetic algorithms in vector space models. TR LISO45/I592001, Department of IS, University of Pittsburg (1992)

    Google Scholar 

  111. L.A. Zadeh: Fuzzy sets. Information and Control, 8, 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  112. L.A. Zadeh: Fuzzy logic, neural networks, and soft computing. Communications of the ACM, 37, 77–84 (1994)

    Article  Google Scholar 

  113. L.A. Zadeh: A new direction in AI: Towards a computational theory of perceptions. AI Magazine, 22, 73–84 (2001)

    Google Scholar 

  114. W. Ziarko, N. Shan: KDD-R: A comprehensive system for knowledge discovery in databases using rough sets. In: Proc. Third International Workshop on Rough Sets and Soft Computing (RSSC’94, 1994) pp. 164-173

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Pal, S.K., Mitra, S., Mitra, P. (2004). Soft Computing Pattern Recognition, Data Mining and Web Intelligence. In: Intelligent Technologies for Information Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-07952-2_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-07952-2_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-07378-6

  • Online ISBN: 978-3-662-07952-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics