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Data Mining: Opportunities and Challenges
Data Mining: Opportunities and Challenges
ISBN: 1591400511
EAN: 2147483647
Year: 2003
Pages: 194
Authors:
John Wang
BUY ON AMAZON
Back Cover
Table of Contents
Data MiningOpportunities and Challenges
Preface
Chapter I: A Survey of Bayesian Data Mining
SCHOOLS OF STATISTICS
DATA MODEL
LOCAL GRAPHICAL MODEL CHOICE
GLOBAL GRAPHICAL MODEL CHOICE
NON-CATEGORICAL VARIABLES
CASE 1: PERSONALIZED MEDIA DISTRIBUTION
CASE 2: SCHIZOPHRENIA RESEARCH - CAUSALITY IN COMPLEX SYSTEMS
CONCLUSIONS
ACKNOWLEDGMENTS
REFERENCES
Chapter II: Control of Inductive Bias in Supervised Learning Using Evolutionary ComputationA Wrapper-Based Approach
BACKGROUND
METHODOLOGIES
RESULTS
ACKNOWLEDGMENTS
ENDNOTES
REFERENCES
Chapter III: Cooperative Learning and Virtual Reality-Based Visualization for Data Mining
COOPERATIVE DATA MINING
VISUAL DATA MINING
THREE-DIMENSIONAL VISUALIZATION, VIRTUAL REALITY AND DATA MINING
CONCLUSIONS
REFERENCES
Chapter IV: Feature Selection in Data Mining
EVOLUTIONARY LOCAL SELECTION ALGORITHMS (ELSA)
FEATURE SELECTION IN SUPERVISED LEARNING
FEATURE SELECTION IN UNSUPERVISED LEARNING
FEATURE SELECTION FOR ENSEMBLES
CONCLUSIONS
ENDNOTES
REFERENCES
Chapter V: Parallel and Distributed Data Mining through Parallel Skeletons and Distributed Objects
PARALLEL AND DISTRIBUTED DATA MINING
STRUCTURED PARALLEL PROGRAMMING
STRUCTURED PARALLEL DATA MINING ALGORITHMS
ADVANTAGES OF STRUCTURE PARALLELISM
CONCLUSIONS
REFERENCES
Chapter VI: Data Mining Based on Rough Sets
ROUGH SET THEORY
VARIABLE PRECISION ROUGH SET MODEL (VPRSM)
DATA MINING SYSTEM LERS
APPLICATIONS
CONCLUSIONS
REFERENCES
Chapter VII: The Impact of Missing Data on Data Mining
DATA MINING WITH INCONSISTENT DATAMISSING DATA
METHODS OF ADDRESSING MISSING DATA
THE IMPACT OF MISSING DATA ON DATA-MINING ALGORITHMS
FUTURE TRENDS
CONCLUSIONS
REFERENCES
Chapter VIII: Mining Text Documents for Thematic Hierarchies Using Self-Organizing Maps
RELATED WORK
GENERATING CLUSTERS
DEVELOPING THEMATIC HIERARCHIES FOR TEXT DOCUMENTS
EXPERIMENTAL RESULTS
FUTURE WORK
CONCLUSIONS
REFERENCES
Chapter IX: The Pitfalls of Knowledge Discovery in Databases and Data Mining
ORGANIZATIONAL ISSUES
STATISTICAL ISSUES
DATA ACCURACY AND STANDARDIZATION
TECHNICAL ISSUES
CONCLUSION
REFERENCES
Chapter X: Maximum Performance Efficiency Approaches for Estimating Best Practice Costs
MOTIVATION
BASIC COST MODELS AND ESTIMATION MODELS
APPLICATION TO THE DATA
ESTIMATION CRITERION QUALITY ISSUES
LIMITATIONS
EXTENSIONS TO OTHER BASIC COST MODELS
DATA-MINING APPLICATIONS AND CONSIDERATIONS
CONCLUSIONS
REFERENCES
APPENDIX
Chapter XI: Bayesian Data Mining and Knowledge Discovery
THE BAYESIAN APPROACH TO PROBABILITY
BAYESIAN CLASSIFICATION
BAYESIAN BELIEF NETWORKS
MARKOV CHAIN MONTE CARLO TECHNIQUES
CONCLUDING REMARKS
ENDNOTES
REFERENCES
Chapter XII: Mining Free Text for Structure
RELATED WORK
MINING NEWSGROUPS EXPERTISE
MINING FAQS FOR STRUCTURE
EVALUATION
FUTURE TRENDS
CONCLUSION
ENDNOTES
ACKNOWLEDGMENTS
REFERENCES
APPENDIX
Chapter XIII: Query-By-Structure Approach for the Web
BACKGROUND
QUERY-BY-STRUCTURE SYSTEMS
FUTURE TRENDS
CONCLUSION
REFERENCES
Chapter XIV: Financial Benchmarking Using Self-Organizing MapsStudying the International Pulp and Paper Industry
METHODOLOGY
COMPANIES INCLUDED
CONSTRUCTING THE MAPS
RESULTS
CONCLUSIONS
FUTURE RESEARCH IN THIS AREA
ACKNOWLEDGMENTS
REFERENCES
APPENDIX: THE FEATURE PLANES OF THE FINAL MAP
Chapter XV: Data Mining in Health Care Applications
DATA MINING IN HEALTH CARE
COMMUNITY HEALTH INFORMATION NETWORKS (CHINS)
FINDINGS USING A CASE METHODOLOGY
HOW THE PLATFORM CAN BE USED
DATA-MINING IMPLICATIONS: CONTROVERSIES AND ISSUES ASSOCIATED WITH CHINS
CONCLUSION
ENDNOTE
REFERENCES
Chapter XVI: Data Mining for Human Resource Information Systems
BACKGROUND
APPLYING DATA MINING TECHNIQUES TO HR INFORMATION SYSTEMS
PRACTICAL APPLICATIONS OF DATA MINING HR INFORMATION
CONCLUSIONS
REFERENCES
Chapter XVII: Data Mining in Information Technology and Banking Performance
DATA ENVELOPMENT ANALYSIS
THE MODEL
APPLICATION
CONCLUSIONS
REFERENCES
Chapter XVIII: Social, Ethical and Legal Issues of Data Mining
WHAT CAN BE INFERRED FROM DATA?
ETHICAL ISSUES
SOCIAL IMPLICATIONS
LEGAL ISSUES
FUTURE TRENDS
CONCLUSION
REFERENCES
Chapter XIX: Data Mining in Designing an Agent-Based DSS
USE OF DATA MINING IN DESIGNING THE SYSTEM KNOWLEDGE BASE
USE OF DATA MINING IN DESIGNING THE LEARNING PROCESS
MULTIAGENT-BASED ARCHITECTURE FOR THE DYNAMIC DSS
CONCLUSIONS
REFERENCES
Chapter XX: Critical and Future Trends in Data Mining-A Review of Key Data Mining TechnologiesApplications
TEXT DATA MINING (TDM)
DISTRIBUTEDCOLLECTIVE DM
UBIQUITOUS DM (UDM)
HYPERTEXT AND HYPERMEDIA DATA MINING
VISUAL DM
MULTIMEDIA DM
SPATIAL AND GEOGRAPHIC DM
TIME SERIESSEQUENCE DM
DM TRENDS: METHODS AND TECHNIQUES
DM FOR BIOINFORMATICS
SUMMARY
REFERENCES
Index
Index A
Index B
Index C
Index D
Index E
Index F
Index G
Index H
Index I
Index J
Index K
Index L
Index M
Index N
Index O
Index P
Index Q
Index R
Index S
Index T
Index U
Index V
Index W
Index X-Y
Index Z
List of Figures
Data Mining: Opportunities and Challenges
ISBN: 1591400511
EAN: 2147483647
Year: 2003
Pages: 194
Authors:
John Wang
BUY ON AMAZON
MySQL Stored Procedure Programming
Conclusion
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Tuning Anti-Joins Using Subqueries
Variables
Qshell for iSeries
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The Integrated File System
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WebLogic: The Definitive Guide
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Examples
Microsoft Windows Server 2003(c) TCP/IP Protocols and Services (c) Technical Reference
Wide Area Network (WAN) Technologies
Point-to-Point Protocol (PPP)
Internet Group Management Protocol (IGMP)
Transmission Control Protocol (TCP) Connections
Virtual Private Networks (VPNs)
GO! with Microsoft Office 2003 Brief (2nd Edition)
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Project 2C. Population Growth
Objective 9. Use Compound Criteria
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AutoCAD 2005 and AutoCAD LT 2005. No Experience Required
Getting to Know AutoCAD
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