SpringerLink
Log in

A Time-Completeness Tradeoff on Fuzzy Web-Browsing Mining

  • Chapter
Fuzzy Logic and the Internet

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 137))

  • 241 Accesses

Abstract

World-wide-web applications have grown very rapidly and have made a significant impact on computer systems. Among them, web browsing for useful information may be most commonly seen. Due to its tremendous amounts of use, efficient and effective web retrieval has thus become a very important research topic in this field. In the past, we proposed a web-mining algorithm for extracting interesting browsing patterns from log data in web servers. It integrated fuzzy-set concepts and data mining approach to achieve this purpose. In that algorithm, each web page used only the linguistic term with the maximum cardinality in the mining process. The number of items was thus the same as that of the original web page, making the processing time reduced. The fuzzy browsing patterns derived in this way are, however, not complete, meaning some possible patterns may be missed. This paper thus modifies it and proposes a new fuzzy web-mining algorithm for extracting all possible fuzzy interesting knowledge from log data in web servers. The proposed algorithm can derive a more complete set of browsing patterns but with more computation time than the previous method. Trade-off thus exists between the computation time and the completeness of browsing patterns. Choosing an appropriate mining method thus depends on the requirements of the application domains.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (Canada)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (Canada)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** 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.

Similar content being viewed by others

References

  1. Agrawal R, Srikant R (1995) Mining sequential patterns. The Eleventh International Conference on Data Engineering 3–14

    Google Scholar 

  2. Blishun AF (1987) Fuzzy learning models in expert systems. Fuzzy Sets and Systems 22:57–70

    Article  Google Scholar 

  3. Campos LM de and Moral S (1993) Learning rules for a fuzzy inference model. Fuzzy Sets and Systems 59:247–257

    Article  MathSciNet  MATH  Google Scholar 

  4. Chang RLP, Pavliddis T (1977) Fuzzy decision tree algorithms. IEEE Transactions on Systems. Man and Cybernetics 7:28–35

    Article  MATH  Google Scholar 

  5. Chen MS, Park JS, Yu PS (1998) Efficient data mining for path taversal patterns. IEEE Transactions on Knowledge and Data Engineering 10:209–221

    Article  Google Scholar 

  6. Chen L, Sycara K (1998) Web Mate: A personal agent for browsing and searching. The Second International Conference on Autonomous Agents. ACM

    Google Scholar 

  7. Clair C, Liu C, Pissinou N (1998) Attribute weighting: a method of applying domain knowledge in the decision tree process. The Seventh International Conference on Information and Knowledge Management. 259–266

    Google Scholar 

  8. Clark P, Niblett T (1989) The CN2 induction algorithm. Machine Learning 3:261–283

    Google Scholar 

  9. Cohen E, Krishnamurthy B, Rexford J (1999) Efficient algorithms for predicting requests to web servers. The Eighteenth IEEE Annual Joint Conference on Computer and Communications Societies 1:284–293

    Google Scholar 

  10. Cooley R, Mobasher B, Srivastava J (1997) Grou** web page references into transactions for mining world wide web browsing patterns. Knowledge and Data Engineering Exchange Workshop 2–9

    Google Scholar 

  11. Cooley R, Mobasher B, Srivastava J (1997) Web mining: information and pattern discovery on the world wide web. The Ninth IEEE International Conference on Tools with Artificial Intelligence 558–567

    Google Scholar 

  12. Delgado M, Gonzalez A (1993) An inductive learning procedure to identify fuzzy systems. Fuzzy Sets and Systems 55:121–132

    Article  MathSciNet  Google Scholar 

  13. Gonzalez A (1995) A learning methodology in uncertain and imprecise environments. International Journal of Intelligent Systems 10: 357–371

    Article  MATH  Google Scholar 

  14. Graham I and Jones PL (1988) Expert Systems — Knowledge, Uncertainty and Decision. Chapman and Computing, Boston 117–158

    Google Scholar 

  15. Hong TP, Chen JB (1999) Finding relevant attributes and membership functions. Fuzzy Sets and Systems 103(3):389–404

    Article  Google Scholar 

  16. Hong TP, Chen JB (2000) Processing individual fuzzy attributes for fuzzy rule induction. Fuzzy Sets and Systems 112(1):127–140

    Article  Google Scholar 

  17. Hong TP, Lee CY (1996) Induction of fuzzy rules and membership functions from training examples. Fuzzy Sets and Systems 84:33–47

    Article  MathSciNet  MATH  Google Scholar 

  18. Hong TP, Kuo CS, Chi SC (1999) A data mining algorithm for transaction data with quantitative values. Intelligent Data Analysis 3(5): 363–376

    Article  MATH  Google Scholar 

  19. Hong TP, Lin KY, Wang SL (2002) Mining linguistic browsing patterns in the world wide web. Soft Computing 6(5):329–336

    Article  MATH  Google Scholar 

  20. Hong TP, Tseng SS (1997) A generalized version space learning algorithm for noisy and uncertain data. IEEE Transactions on Knowledge and Data Engineering 9(2):336–340

    Article  Google Scholar 

  21. Hou RH, Hong TP, Tseng SS, Kuo SY (1997) A new probabilistic induction method. Journal of Automatic Reasoning 18:5–24

    Article  MATH  Google Scholar 

  22. Kandel A (1992) Fuzzy Expert Systems. CRC Press, Boca Raton 8–19

    Google Scholar 

  23. Mamdani EH (1974) Applications of fuzzy algorithms for control of simple dynamic plants. IEEE Proceedings 1585–1588

    Google Scholar 

  24. Quinlan JR (1987) Decision tree as probabilistic classifier. The Fourth International Machine Learning Workshop. Morgan Kaufmann, San Mateo CA 31–37

    Google Scholar 

  25. Quinlan JR (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo CA

    Google Scholar 

  26. Rives J (1990) FID3: fuzzy induction decision tree. The First International Symposium on Uncertainty, Modeling and Analysis 457–462

    Google Scholar 

  27. Wang CH, Hong TP, Tseng SS (1996) Inductive learning from fuzzy examples. The Fifth IEEE International Conference on Fuzzy Systems, New Orleans 13–18

    Google Scholar 

  28. Wang CH, Liu JF, Hong TP, Tseng SS (1999) A fuzzy inductive learning strategy for modular rules. Fuzzy Sets and Systems 103(1):91–105

    Article  Google Scholar 

  29. Weber R (1992) Fuzzy-ID3: a class of methods for automatic knowledge acquisition. The Second International Conference on Fuzzy Logic and Neural Networks, Iizuka Japan 265–268

    Google Scholar 

  30. Yuan Y, Shaw MJ (1995) Induction of fuzzy decision trees. Fuzzy Sets and Systems 69:125–139

    Article  MathSciNet  Google Scholar 

  31. Zadeh LA (1988) Fuzzy logic. IEEE Computer 83–93

    Google Scholar 

  32. Zimmermann HJ (1991) Fuzzy Set Theory and Its Applications. Kluwer Academic Publisher, Boston

    MATH  Google Scholar 

  33. <Reftitle>References</Reftitle>

    Google Scholar 

  34. Makhoul J, Kubala F et al. (2000) Speech and language technologies for audio indexing and retrieval code. In: Proceedings of the IEEE, Volume: 88 Issue: 8, Aug 2000, pp: 1338–1353

    Google Scholar 

  35. Viswanathan M, Beigi H.S.M et al. (1999) Retrieval from spoken documents using content and speaker information. In: ICDAR’99 pp: 567–572

    Google Scholar 

  36. Gauvain J.-L, Lamel L (2000) Large-vocabulary continuous speech recognition: advances and applications. In: Proceedings of the IEEE, Volume: 88 Issue: 8, Aug 2000, pp: 1181–1200

    Google Scholar 

  37. Chih-Chin Liu, Jia-Lien Hsu, Chen A.L.P (1999) An approximate string matching algorithm for content-based music data retrieval. In: IEEE International Conference on Multimedia Computing and Systems, Volume: 1, 1999, pp: 451–456

    Google Scholar 

  38. Delfs C, Jondral F (1997) Classification of piano sounds using time-frequency signal analysis. In: ICASSP-97, Volume: 3 pp: 2093–2096

    Google Scholar 

  39. Paradie M.J, Nawab S.H (1990) The classification of ringing sounds. In: ICASSP-90, pp: 2435–2438

    Google Scholar 

  40. Scheirer E, Slaney M (1997) Construction and evaluation of a robust multifeature speech/music discriminator. In: ICASSP-97, Volume: 2, pp: 1331–1334

    Google Scholar 

  41. Tong Zhang, C.-C. Jay Kuo (1999) Heuristic approach for generic audio data segmentation and annotation. In: ACM Multimedia’99, pp: 67–76

    Google Scholar 

  42. Liu Z, Huang J, Wang Y (1998) Classification TV programs based on audio information using hidden Markov model. In: IEEE Second Workshop on Multimedia Signal Processing, 1998, pp: 27–32

    Chapter  Google Scholar 

  43. Wold E, Blum T, Keislar D, Wheaten J (1996) Content-based classification, search, and retrieval of audio. In: IEEE Multimedia, Volume: 3 Issue: 3, Fall 1996, pp: 27–36

    Google Scholar 

  44. Zhu Liu, Qian Huang (2000) Content-based indexing and retrieval-by-example in audio. In: ICME 2000, Volume: 2, pp: 877–880

    Google Scholar 

  45. Beritelli F, Casale S, Russo M (1995) Multilevel Speech Classification Based on Fuzzy Logic. In: Proceedings of IEEE Workshop on Speech Coding for Telecommunications, 1995, pp: 97–98

    Chapter  Google Scholar 

  46. Zhu Liu, Qian Huang (1998) Classification of audio events in broadcast news. In: IEEE Second Workshop on Multimedia Signal Processing, 1998, pp:364–369

    Google Scholar 

  47. Mingchun Liu, Chunru Wan (2001) A study on content-based classification and retrieval of audio database. In: International Database Engineering and Application Symposium, 2001, pp: 339–345

    Google Scholar 

  48. Li S.Z (2000) Content-based audio classification and retrieval using the nearest feature line method, IEEE Transactions on Speech and Audio Processing, Volume: 8 Issue: 5, Sept 2000, pp: 619–625

    Article  Google Scholar 

  49. Jang J.-S.R (1993) ANFIS: adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man and Cybernetics, 1993, volume: 23, Issue: 3, pp: 665–685

    Article  Google Scholar 

  50. <Reftitle>References</Reftitle>

    Google Scholar 

  51. Attardi, G., Di Marco S., and Salvi, D. (1998). Categorisation by Context. Journal of Universal Compouter Science, 4:719–736.

    Google Scholar 

  52. Boley, D., Gini, M., Gross, R., Hang, E-H., Hasting, K., Karypis, G., Kumar, V., Mobasher, B., and Moore, J. (1999). Partioning-based clustering for Web document categorization Decision Support System, 27 (1999) 329–341.

    Article  Google Scholar 

  53. Chakrabarti, S., Dom, B., Gibson, D., Kleinberg, J., Rahavan, P., and Rajagopalan, S.(1998). Automatic resource list compilation by analyzing hyperlink structure and associated text. Seventh International World Wide Web Conference, 1998.

    Google Scholar 

  54. Chang, C-H., and Hsu, C-C. (1997). Customizable Multi-Engine Search tool with Clustering. Sixth International World Wide Web Conference, April 7–11, 1997 Santa Clara, California, USA.

    Google Scholar 

  55. Cohen, W. (1998). A web-based information system that reasons with structured collections of text. Agents ’98, 1998.

    Google Scholar 

  56. Craven, M., DiPasquo, D., Freitag, D., McCallum, A., Mitchell, T., Nigam, K., and Slattery, S. (1998). Learning to extract symbolic knowledge from the World Wide Web. AAAI-98, 1998.

    Google Scholar 

  57. Hayes, J., and Weinstein, S. P. (1990). CONSTRUE-TIS: A system for contentbased indexing of a database of news stories. Second Annual Conference on Innovative Applications of Artificial Intelligence, 1–5.

    Google Scholar 

  58. Iwayama, M. (1995). Cluster-based text categorization: a comparison of category search strategies. SIGIR-95, pp. 273–280.

    Chapter  Google Scholar 

  59. JDK Java 2 Sun. http://java.sun.com

  60. Kruschwitz, U. (2001). Exploiting Structure for Intelligent Web Search. 2001 IEEE International Confernce on System Science, January 3–6, 2001, Hawaii, IEEE Press.

    Google Scholar 

  61. Lawrence, S. and Giles, C. L. (1999). Nature, 400:107–109. Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99).

    Article  Google Scholar 

  62. Loia, V. and Luongo, P. (2001). Genetic-based Fuzzy Clustering for Automatic Web Document Categorization, 2001 ACM Symposium Applied Computation, March 11–14 2001, Las Vegas, USA, ACM Press.

    Google Scholar 

  63. Loia, V. and Luongo, P. (2001). An Evolutionary Approach to Automatic Web Page Categorization and Updating, 2001 International Conference on Web Intelligence, October 23–26, 2001, Maebashi City, Japan.

    Google Scholar 

  64. Mase, H., Tsuji, H., Kinukawa, H., Hosoya, Y., Koutani, K., and Kiyota, K. (1996). Experimental simulation for automatic patent categorization. Advances in Production Management Systems, 377–382.

    Google Scholar 

  65. McCallum, A., Nigam, K., Rennie, J., and Seymore, K. (1999). A Machine Learning Approach to Building Domain-Specific Search Engine. Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99).

    Google Scholar 

  66. Open Directory Project. URL: http://dmoz.org/about.html

  67. Sahami, M., Yusufali, S., and Baldoando, M. Q., W. (1998) SONIA: A service for organizing networked information autonomously. Third ACM Conference on Digital Libraries.

    Google Scholar 

  68. Selberg, E. (1999) Towards Comprehensive Web Search. PhD thesis, University of Washington.

    Google Scholar 

  69. Selberg, E and Etzioni, O. (2000). On the Instability of Web Search Engine. RIAO2000.

    Google Scholar 

  70. Zamir, O., and Etzioni, O. (1988). Web Document Clustering: A Feasibility Demonstration. SIGIR’98, Melbourne, Australia, ACM Press.

    Google Scholar 

  71. A Lexical Database for English. URL: http://www.cogsci.princeton.edu/wn/

  72. <Reftitle>References</Reftitle>

    Google Scholar 

  73. Agrawal R, Imielinski T, Swami A (1993) Mining Association Rules between Set of Items in Large Databases. Proc. of the 1993 ACM SIGMOD Conference, pp 207–216

    Google Scholar 

  74. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. Proc. Of the 20th VLDB Conference, pp 478–499

    Google Scholar 

  75. Attar R, Fraenkel AS (1977) Local Feedback in Full-Text Retrieval Systems. Journal of the Association for Computing Machinery 24(3):397–417

    Article  MATH  Google Scholar 

  76. Au WH, Chan KCC (1998) An effective algorithm for discovering fuzzy rules in relational databases. Proc. Of IEEE International Conference on Fuzzy Systems, vol II, pp 1314–1319

    Google Scholar 

  77. Baeza-Yates R, Ribeiro-Nieto B (1999) Modern Information Retrieval, Addison-Wesley, USA

    Google Scholar 

  78. Berzal F, Cubero JC, Marín N, Serrano JM (2001) TBAR: An efficient method for association rule mining in relational databases. Data and Knowledge Engineering 37(1):47–84

    Article  MATH  Google Scholar 

  79. Berzal F, Blanco I, Sanchez, Vila MA (2002) Measuring the Accuracy and Importance of Association Rules: A New Framework. Intelligent Data Analysis 6:221–235

    MATH  Google Scholar 

  80. Bodner RC, Song F (1996) Knowledge-Based Approaches to Query Expansion in Information Retrieval. In: McGalla G (ed) Advances in Artificial Intelligence pp 146–158. Springer, New York

    Google Scholar 

  81. Brin S, Motwani JD, Ullman JD, Tsur S (1997) Dynamic itemset counting and implication rules for market basket data. SIGMOD Record 26(2):255–264

    Article  Google Scholar 

  82. Buckley C, Salton G, Allan J, Singhal A (1993) Automatic Query Expansion using SMART: TREC 3″. Proc. of the 3 rd Text Retrieval Conference. NIST Special Publication 500–225, pp 69–80

    Google Scholar 

  83. Buell DA, Kraft DH (1981) Performance Measurement in a Fuzzy Retrieval Environment. Proceedings of the Fourth International Conference on Information Storage and Retrieval, ACM/SIGIR Forum 16(1): 56–62, Oakland, CA

    Google Scholar 

  84. Chen H, Ng T, Martinez J, Schatz BR (1997) A Concept Space Approach to Addressing the Vocabulary Problem in Scientific Information Retrieval: An Experiment on the Worm Community System. Journal of the American Society for Information Science 48(1):17–31

    Article  Google Scholar 

  85. Croft WB, Thompson RH (1987) I3R: A New Approach to the Design of Document Retrieval Systems. Journal of the American Society for Information Science 38(6):389–404

    Article  Google Scholar 

  86. Delgado M, Marín N, Sanchez D, Vila MA (2001). Fuzzy Association Rules: General Model and Applications. IEEE Transactions of Fuzzy Systems (accepted)

    Google Scholar 

  87. Delgado M, Martín-Bautista MJ, Sanchez D, Vila MA (2000). Mining strong approximate dependences from relational databases. Proc. Of IPMU 2000 2:1123–1130. Madrid, Spain

    Google Scholar 

  88. Delgado M, Martín-Bautista MJ, Sanchez D, Vila MA (2001) Mining association rules with improved semantics in medical databases. Artificial Intelligence in Medicine 21:241–245

    Article  Google Scholar 

  89. Delgado M, Martín-Bautista MJ, Sanchez D, Vila MA (2002) Mining Text Data: Special Features and Patterns. Proc. of EPS Exploratory Workshop on Pattern Detection and Discovery in Data Mining, pp 140–153. Imperial College Londres, UK

    Google Scholar 

  90. Delgado M, Sanchez D, Vila MA (2000) Acquisition of fuzzy association rules from medical data. In Barro S, Marín R (eds) Fuzzy Logic in Medicine. PhysicaVerlag

    Google Scholar 

  91. Delgado M, Sanchez D, Vila MA (2000) Fuzzy cardinality based evaluation of quantified sentences. International Journal of Approximate Reasoning 23:23–66

    Article  MathSciNet  MATH  Google Scholar 

  92. Efthimiadis E (1996) Query Expansion. Annual Review of Information Systems and Technology 31:121–187

    Google Scholar 

  93. Feldman R, Fresko M, Kinar Y, Lindell Y, Liphstat 0, Rajman M, Schler Y, Zamir O (1998) Text Mining at the Term Level. Proc. of the 2nd European Symposium of Principles of Data Mining and Knowledge Discovery, pp 65–73

    Google Scholar 

  94. Feldman R, Hirsh H (1996) Mining associations in text in the presence of Background Knowledge. Proc. of the Second International Conference on Knowledge Discovery from Databases

    Google Scholar 

  95. Fu AW, Wong MH, Sze SC, Wong WC, Wong WL, Yu WK (1998) Finding Fuzzy Sets for the Mining of Fuzzy Association Rules for Numerical Attributes. Proc. of Int. Symp. on Intelligent Data Engineering and Learning (IDEAL’98), pp 263–268, Hong Kong

    Google Scholar 

  96. Fu LM, Shortliffe EH (2000) The application of certainty factors to neural computing for rule discovery. IEEE Transactions on Neural Networks 11(3):647–657

    Article  Google Scholar 

  97. Gauch S, Smith JB (1993) An Expert System for Automatic Query Reformulation. Journal of the American Society for Information Science 44(3):124–136

    Article  Google Scholar 

  98. Han J, Pei J, Yin Y (2000)Mining frequent patterns without candidate generation. Proc. ACM SIGMOD Int. Conf. On Management of Data, pp 1–12. Dallas, TX, USA

    Google Scholar 

  99. Harman D (1988) Towards interactive query expansion. Proc. of the Eleventh Annual International ACMSIGIR Conference on Research and Development in Information Retrieval pp 321–331. ACM Press

    Google Scholar 

  100. Hearst M (1999) Untangling Text Data Mining. Proc. of the 37th Annual Meeting of the Association for Computational Linguistics (ACL’99). University of Maryland

    Google Scholar 

  101. Hearst M (2000) Next Generation Web Search: Setting our Sites. IEEE Data Engineering Bulletin, Special issue on Next Generation Web Search, Gravano L (ed)

    Google Scholar 

  102. Houtsma M, Swami A (1995) Set-oriented mining for association rules in relational databases. Proc. Of the 11th International Conference on Data Engineering pp 25–33.

    Google Scholar 

  103. Kodratoff Y (1999) Knowledge Discovery in Texts: A Definition, and Applications. In: Ras ZW, Skowron A (eds) Foundation of Intelligent Systems, Lectures Notes on Artificial Intelligence 1609. Springer Verlag

    Google Scholar 

  104. Kraft D, Petry FE, Buckles BP, Sadasivan T (1997) Genetic Algorithms for Query Optimization in Information Retrieval: Relevance Feedback. In: Sanchez E, Shibata T, Zadeh LA, (eds) Genetic Algorithms and Fuzzy Logic Systems,

    Google Scholar 

  105. Kraft D, Petry FE, Buckles BP, Sadasivan T (1997) Advances in Fuzziness: Applications and Theory 7:157–173, World Scientific

    Google Scholar 

  106. Lin SH, Shih CS, Chen MC, Ho JM, Ko MT, Huang YM (1998) Extracting Classification Knowledge of Internet Documents with Mining Term Associations: A Semantic Approach. Proc. of ACM/SIGIR’98 pp 241–249. Melbourne, Australia

    Google Scholar 

  107. Mannila H, Toivonen H, Verkamo I (1994) Efficient algorithms for discovering association rules. Proc. Of AAAI Workshop on Knowledge Discovery in Databases pp 181–192

    Google Scholar 

  108. Miller G (1990) WordNet: An on-line lexical database. International Journal of Lexicography 3(4)

    Google Scholar 

  109. Mitra M, Singhal A, Buckley C (1998) Improving Automatic Query Expansion. Proc. Of ACM SIGIR pp 206–214. Melbourne, Australia

    Google Scholar 

  110. Park JS, Chen MS, Yu PS (1995) An effective hash based algorithm for mining association rules. SIGMOD Record 24(2):175–186

    Article  Google Scholar 

  111. Peat HJ, Willet P (1991) The limitations of term co-occurrence Data for Query Expansion in Document Retrieval Systems. Journal of the American Society for Information Science 42(5):378–383

    Article  Google Scholar 

  112. Piatetsky-Shapiro G (1991) Discovery, Analysis, and Presentation of Strong Rules. In: Piatetsky-Shapiro G, Frawley WJ (eds) Knowledge Discovery in Databases, AAAI/MIT Press

    Google Scholar 

  113. Porter MF (1980) An algorithm for suffix strip**. Program 14(3):130–137

    Article  Google Scholar 

  114. Qui Y, Frei HP (1993) Concept Based Query Expansion. Proc. Of the Sixteenth Annual International ACM-SIGIR’93 Conference on Research and Development in Information Retrieval pp 160–169

    Google Scholar 

  115. Rajman M, Besançon R (1997) Text Mining: Natural Language Techniques and Text Mining Applications. Proc. of the 3d International Conference on Database Semantics (DS-7)Chapam & Hall IFIP Proceedings serie

    Google Scholar 

  116. Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval. Information Processing and Management 24(5):513–523

    Article  Google Scholar 

  117. Salton G, McGill MJ (1983) Introduction to Modern Information Retrieval. McGraw-Hill

    MATH  Google Scholar 

  118. Shortliffe E, Buchanan B (1975) A model of inexact reasoning in medicine. Mathematical Biosciences 23:351–379

    Article  MathSciNet  Google Scholar 

  119. Srinivasan P, Ruiz ME, Kraft DH, Chen J (2001) Vocabulary mining for information retrieval: rough sets and fuzzy sets. Information Processing and Management 37:15–38

    Article  MATH  Google Scholar 

  120. Van Rijsbergen CJ, Harper DJ, Porter MF (1981) The selection of good search terms. Information Processing and Management 17:77–91

    Article  Google Scholar 

  121. Vélez B, Weiss R, Sheldon MA, Gifford DK (1997) Fast and Effective Query Refinement. Proc. Of the 20th ACM Conference on Research and Development in Information Retrieval (SIGIR’97). Philadelphia, Pennsylvania

    Google Scholar 

  122. Voorhees EM (1994) Query expansion using Lexical-Semantic Relations. ACM SIGIR pp 61–70

    Google Scholar 

  123. Xu J, Croft WB (1996) Query Expansion Using Local and Global Document Analysis. Proc. of the Nineteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval pp 4–11

    Google Scholar 

  124. Zadeh LA (1983) A computational approach to fuzzy quantifiers in natural languages. Computing and Mathematics with Applications 9(1):149–184

    Article  MathSciNet  MATH  Google Scholar 

  125. <Reftitle>References</Reftitle>

    Google Scholar 

  126. Fair, Isaac and Co.: http://www.fairisaac.com/.

    Google Scholar 

  127. Bonissone P.P., Decker K.S. (1986) Selecting Uncertainty Calculi and Granularity: An Experiment in Trading; Precision and Complexity, in Uncertainty in Artificial Intelligence (L. N. Kanal and J. F. Lemmer, Eds.), Amsterdam.

    Google Scholar 

  128. Fagin R. (1998) Fuzzy Queries in Multimedia Database Systems, Proc. ACM Symposium on Principles of Database Systems, pp. 1–10.

    Google Scholar 

  129. Fagin R. (1999) Combining fuzzy information from multiple systems. J. Computer and System Sciences 58, pp 83–99.

    Article  MathSciNet  MATH  Google Scholar 

  130. Mizumoto M. (1989) Pictorial Representations of Fuzzy Connectives, Part I: Cases of T-norms, T-conorms and Averaging Operators, Fuzzy Sets and Systems 31, pp. 217–242.

    Article  MathSciNet  Google Scholar 

  131. Nikravesh M. (2001a) Perception-based information processing and retrieval: application to user profiling, 2001 research summary, EECS, ERL, University of California, Berkeley, BT-BISC Project. http://zadeh.cs.berkeley.edu/ & http://www-bisc.cs.berkeley.edu/.

    Google Scholar 

  132. Nikravesh M. (2001b) Credit Scoring for Billions of Financing Decisions, Joint 9th IFSA World Congress and 20th NAFIPS International Conference. IFSA/NAFIPS 2001“ Fuzziness and Soft Computing in the New Millenium”, Vancouver, Canada, July 25–28, 2001.

    Google Scholar 

  133. Stanford University Admission, http://www.stanford.edu/home/stanford/facts/undergraduate.html.

  134. U.S. Citizens for Fair Credit Card Terms; http://www.cardratings.org/cardrepfr.html.

  135. University of California-Berkeley, Office of Undergraduate Admission, http://advising.berkeley.edu/ouars/.

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung, 811, Taiwan, R.O.C.

    Tzung-Pei Hong

  2. Telecommunication Laboratories, Chunghwa Telecom Ltd., Chung-li, 326, Taiwan, R.O.C.

    Kuei-Ying Lin

  3. Department of Information Management, National University of Kaohsiung, Kaohsiung, 811, Taiwan, R.O.C.

    Shyue-Liang Wang

Authors
  1. Tzung-Pei Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kuei-Ying Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shyue-Liang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dipto. Matematica e Informatica, Universita di Salerno, Via S. Allende, 84081, Baronissi, Italy

    Vincenzo Loia

  2. Dept. Electrical Engineering and Computer Science — EECS, University of California, 94720, Berkeley, CA, USA

    Masoud Nikravesh  & Lotfi A. Zadeh  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Hong, TP., Lin, KY., Wang, SL. (2004). A Time-Completeness Tradeoff on Fuzzy Web-Browsing Mining. In: Loia, V., Nikravesh, M., Zadeh, L.A. (eds) Fuzzy Logic and the Internet. Studies in Fuzziness and Soft Computing, vol 137. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39988-9_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-39988-9_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05770-0

  • Online ISBN: 978-3-540-39988-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation