(ed.) Intelligent Agents for Data Mining and Information Retrieval 

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• 1. Table of Contents

• 2. BackCover

  Intelligent Agents for Data Mining and Information Retrieval

• 3. Intelligent Agents for Data Mining and Information Retrieval

• 4. Preface

  Acknowledgments

• 5. Chapter I: Potential Cases, Database Types, and Selection Methodologies for Searching Distributed Text Databases

  INTRODUCTION

• 6. PROBLEM DESCRIPTION

  PROBLEM DESCRIPTION

• 7. POTENTIAL SELECTION CASES IN DTDS

  POTENTIAL SELECTION CASES IN DTDS

• 8. THE CLASSIFICATION OF TYPES OF DTDS

  THE CLASSIFICATION OF TYPES OF DTDS

• 9. RELATIONSHIPS BETWEEN POTENTIAL SELECTION CASES AND DTD TYPES

  RELATIONSHIPS BETWEEN POTENTIAL SELECTION CASES AND DTD TYPES

• 10. NECESSARY CONSTRAINTS OF SELECTION METHODS IN DTDS

  NECESSARY CONSTRAINTS OF SELECTION METHODS IN DTDS General Necessary Constraints for All Selection Methods in DTDs Special Necessary Constraints of Selection Methods for Each Selection Case

• 11. CONCLUSION AND FUTURE WORK

  CONCLUSION AND FUTURE WORK

• 12. ACKNOWLEDGMENTS

  ACKNOWLEDGMENTS

• 13. REFERENCES

  REFERENCES

• 14. Chapter II: Computational Intelligence Techniques Driven Intelligent Agents for Web Data Mining and Information Retrieval

  Computational Intelligence Techniques Driven Agents for Web Data Mining and Information Retrieval Intelligent Agents for Data Mining and Information Retrieval

• 15. SEARCH ENGINES

  SEARCH ENGINES

• 16. INTELLIGENT AGENTS

  INTELLIGENT AGENTS

• 17. INTELLIGENT AGENTS FOR INFORMATION FILTERING AND DATA MINING

  INTELLIGENT AGENTS FOR INFORMATION FILTERING AND DATA MINING

• 18. EVOLUTIONARY COMPUTING, FUZZY LOGIC AND I-AGENTS FOR INFORMATION FILTERING

  EVOLUTIONARY COMPUTING FUZZY LOGIC AND I-AGENTS FOR INFORMATION FILTERING

• 19. SIMULATION RESULTS

  SIMULATION RESULTS

• 20. CONCLUSION

  CONCLUSION

• 21. ACKNOWLEDGMENT

  ACKNOWLEDGMENT

• 22. REFERENCES

  REFERENCES

• 23. ELECTRONIC REFERENCES

  ELECTRONIC REFERENCES

• 24. Chapter III: A Multi-Agent Approach to Collaborative Knowledge Production

  INTRODUCTION Collaborative Knowledge Production A Multi-Agent Approach

• 25. A KNOWLEDGE PRODUCTION ARCHITECTURE

  A KNOWLEDGE PRODUCTION ARCHITECTURE Knowledge Marts Knowledge Consolidation Multi-Level Architecture Agent Interaction in a Knowledge Mart Interaction Protocol

• 26. EVALUATION IN AN INSTRUCTIONAL DESIGN SCENARIO

  EVALUATION IN AN INSTRUCTIONAL DESIGN SCENARIO Evaluation Scenario

• 27. CONCLUSION

  CONCLUSION

• 28. REFERENCES

  REFERENCES

• 29. Chapter IV: Customized Recommendation Mechanism Based on Web Data Mining and Case-Based Reasoning

  INTRODUCTION

• 30. BACKGROUND

  (c) Compare and contrast data mining and intelligent agents?

• 31. METHODOLOGY

  METHODOLOGY

• 32. IMPLEMENTATION

  IMPLEMENTATION Phase I: Association Rule Generation Phase II III: Case Generation Construction of Hybrid Knowledge Base Phase IV: Hybrid Recommendation

• 33. CONCLUSION

  CONCLUSION

• 34. ACKNOWLEDGMENTS

  ACKNOWLEDGMENTS

• 35. REFERENCES

  REFERENCES

• 36. Chapter V: Rule-Based Parsing for Web Data Extraction

  OVERVIEW

• 37. INTRODUCTION

  INTRODUCTION

• 38. GENERIC MULTI-AGENT WEB ARCHITECTURE

  GENERIC MULTI-AGENT WEB ARCHITECTURE Characteristics to Implement Robust MAS-Web Systems

• 39. WEBPARSER: SEMI-AUTOMATIC WEB KNOWLEDGE EXTRACTION

  WEBPARSER: SEMI-AUTOMATIC WEB KNOWLEDGE EXTRACTION The WebParser Architecture Definition of the Sets Rules in the WebParser

• 40. DEPLOYING A WEB-MAS USING THE WEBPARSER

  DEPLOYING A WEB-MAS USING THE WEBPARSER SimpleNews: A MetaSearch System for Electronic News WebParser Integration into SimpleNews Verification and Test

• 41. CONCLUSIONS AND FUTURE WORK

  CONCLUSIONS AND FUTURE WORK

• 42. REFERENCES

  REFERENCES

• 43. Chapter VI: Multilingual Web Content Mining - A User-Oriented Approach

  INTRODUCTION

• 44. BACKGROUND

  BACKGROUND

• 45. USER-ORIENTED, CONCEPT-BASED APPROACH

  USER-ORIENTED CONCEPT-BASED APPROACH An Overview Development of a Multilingual Concept Space Development of a Concept-Based Multilingual Text Classifier Personalization of the Concept-Based Browsing Interface

• 46. TOWARD INTELLIGENT PERSONAL MULTILINGUAL WEB MINING AGENT

  TOWARD INTELLIGENT PERSONAL MULTILINGUAL WEB MINING AGENT

• 47. CONCLUSION

  CONCLUSION

• 48. REFERENCES

  REFERENCES

• 49. Chapter VII: A Textual Warehouse Approach - A Web Data Repository

  INTRODUCTION

• 50. BACKGROUND

  BACKGROUND

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