2010, ISBN: 9788120341340

PHI Learning, 2010. First edition. Softcover. New. .. .. This book brings together the latest research—in statistics, databases, machine learning, and artificial intel… Mehr…

PHI Learning, 2010. First edition. Softcover. New. .. .. This book brings together the latest research—in statistics, databases, machine learning, and artificial intelligence—that are part of the exciting and rapidly growing field of Knowledge Discovery and Data Mining. The chapters of this book, organized into eight sections, span fundamental issues of knowledge discovery, classification and clustering, trend and deviation analysis, dependency derivation, integrated discovery systems, augmented database systems, and application case studies: Part One deals with fundamental issues in discovery. Part Two examines specific techniques for data mining. Part Three presents methods for dealing with trend and deviation analysis. Part Four focuses on data mining techniques for deriving dependencies. Part Five discusses integrated discovery systems. Part Six presents approaches for next-generation database systems. Part Seven presents several real and successful applications. The Appendices provides a list of terms used in the literature of this fast-expanding field, and a list of online resources for the KDD researcher. CONTENTS: Foreword. Preface. 1. From Data Mining to Knowledge Discovery: An Overview. I. Foundations—2. The Process of Knowledge Discovery in Databases: A Human-Centered Approach. 3. Graphical Models for Discovering Knowledge. 4. A Statistical Perspective on Knowledge Discovery in Databases. II. Classification and Clustering—5. Inductive Logic Programming and Knowledge Discovery in Databases. 6. Bayesian Classification (AutoClass): Theory and Results. 7. Discovering Informative Patterns and Data Cleaning. 8. Transforming Rules and Trees into Comprehensible Knowledge Structures. III. Trend and Deviation Analysis: 9. Finding Patterns in Time Series: A Dynamic Programming Approach. 10. Explora: A Multipattern and Multistrategy Discovery Assistant. IV. Dependency Derivation—11. Bayesian Networks for Knowledge Discovery. 12. Fast Discovery of Association Rules. 13. From Contingency Tables to Various Forms of Knowledge in Databases. V. Integrated Discovery Systems—14. Integrating Inductive and Deductive Reasoning for Data Mining. 15. Metaqueries for Data Mining. 16. Exploration of the Power of Attribute-Oriented Induction in Data Mining. VI. Next Generation Database Systems—17. Using Inductive Learning To Generate Rules for Semantic Query Optimization. 18. Data Surveyor: Searching the Nuggets in Parallel. VII. KDD Applications—19. Automating the Analysis and Cataloging of Sky Surveys. 20. Selecting and Reporting What is Interesting: The KEFIR Application to Healthcare Data. 21. Modeling Subjective Uncertainty in Image Annotation. 22. Predicting Equity Returns from Securities Data with Minimal Rule Generation. 23. From Data Mining to Knowledge Discovery: Current Challenges and Future Directions. VIII. Appendices—A: Knowledge Discovery in Databases Terminology. B: Data Mining and Knowledge Discovery Internet Resources. About the Editors. Index., PHI Learning, 2010<

2010, ISBN: 9788120341340

PHI Learning, 2010. First edition. Softcover. New. .. .. This book brings together the latest research—in statistics, databases, machine learning, and artificial intel… Mehr…

PHI Learning, 2010. First edition. Softcover. New. .. .. This book brings together the latest research—in statistics, databases, machine learning, and artificial intelligence—that are part of the exciting and rapidly growing field of Knowledge Discovery and Data Mining. The chapters of this book, organized into eight sections, span fundamental issues of knowledge discovery, classification and clustering, trend and deviation analysis, dependency derivation, integrated discovery systems, augmented database systems, and application case studies: Part One deals with fundamental issues in discovery. Part Two examines specific techniques for data mining. Part Three presents methods for dealing with trend and deviation analysis. Part Four focuses on data mining techniques for deriving dependencies. Part Five discusses integrated discovery systems. Part Six presents approaches for next-generation database systems. Part Seven presents several real and successful applications. The Appendices provides a list of terms used in the literature of this fast-expanding field, and a list of online resources for the KDD researcher. CONTENTS: Foreword. Preface. 1. From Data Mining to Knowledge Discovery: An Overview. I. Foundations—2. The Process of Knowledge Discovery in Databases: A Human-Centered Approach. 3. Graphical Models for Discovering Knowledge. 4. A Statistical Perspective on Knowledge Discovery in Databases. II. Classification and Clustering—5. Inductive Logic Programming and Knowledge Discovery in Databases. 6. Bayesian Classification (AutoClass): Theory and Results. 7. Discovering Informative Patterns and Data Cleaning. 8. Transforming Rules and Trees into Comprehensible Knowledge Structures. III. Trend and Deviation Analysis: 9. Finding Patterns in Time Series: A Dynamic Programming Approach. 10. Explora: A Multipattern and Multistrategy Discovery Assistant. IV. Dependency Derivation—11. Bayesian Networks for Knowledge Discovery. 12. Fast Discovery of Association Rules. 13. From Contingency Tables to Various Forms of Knowledge in Databases. V. Integrated Discovery Systems—14. Integrating Inductive and Deductive Reasoning for Data Mining. 15. Metaqueries for Data Mining. 16. Exploration of the Power of Attribute-Oriented Induction in Data Mining. VI. Next Generation Database Systems—17. Using Inductive Learning To Generate Rules for Semantic Query Optimization. 18. Data Surveyor: Searching the Nuggets in Parallel. VII. KDD Applications—19. Automating the Analysis and Cataloging of Sky Surveys. 20. Selecting and Reporting What is Interesting: The KEFIR Application to Healthcare Data. 21. Modeling Subjective Uncertainty in Image Annotation. 22. Predicting Equity Returns from Securities Data with Minimal Rule Generation. 23. From Data Mining to Knowledge Discovery: Current Challenges and Future Directions. VIII. Appendices—A: Knowledge Discovery in Databases Terminology. B: Data Mining and Knowledge Discovery Internet Resources. About the Editors. Index. Printed Pages: 628., PHI Learning, 2010<

2010, ISBN: 9788120341340

New Softcover NA This book brings together the latest researchaEUR"in statistics, databases, machine learning, and artificial intelligenceaEUR"that are part of the exciting and rapidly gr… Mehr…

New Softcover NA This book brings together the latest researchaEUR"in statistics, databases, machine learning, and artificial intelligenceaEUR"that are part of the exciting and rapidly growing field of Knowledge Discovery and Data Mining. The chapters of this book, organized into eight sections, span fundamental issues of knowledge discovery, classification and clustering, trend and deviation analysis, dependency derivation, integrated discovery systems, augmented database systems, and application case studies: Part One deals with fundamental issues in discovery. Part Two examines specific techniques for data mining. Part Three presents methods for dealing with trend and deviation analysis. Part Four focuses on data mining techniques for deriving dependencies. Part Five discusses integrated discovery systems. Part Six presents approaches for next-generation database systems. Part Seven presents several real and successful applications. The Appendices provides a list of terms used in the literature of this fast-expanding field, and a list of online resources for the KDD researcher. CONTENTS: Foreword. Preface. 1. From Data Mining to Knowledge Discovery: An Overview. I. FoundationsaEUR"2. The Process of Knowledge Discovery in Databases: A Human-Centered Approach. 3. Graphical Models for Discovering Knowledge. 4. A Statistical Perspective on Knowledge Discovery in Databases. II. Classification and ClusteringaEUR"5. Inductive Logic Programming and Knowledge Discovery in Databases. 6. Bayesian Classification (AutoClass): Theory and Results. 7. Discovering Informative Patterns and Data Cleaning. 8. Transforming Rules and Trees into Comprehensible Knowledge Structures. III. Trend and Deviation Analysis: 9. Finding Patterns in Time Series: A Dynamic Programming Approach. 10. Explora: A Multipattern and Multistrategy Discovery Assistant. IV. Dependency DerivationaEUR"11. Bayesian Networks for Knowledge Discovery. 12. Fast Discovery of Association Rules. 13. From Contingency Tables to Various Forms of Knowledge in Databases. V. Integrated Discovery SystemsaEUR"14. Integrating Inductive and Deductive Reasoning for Data Mining. 15. Metaqueries for Data Mining. 16. Exploration of the Power of Attribute-Oriented Induction in Data Mining. VI. Next Generation Database SystemsaEUR"17. Using Inductive Learning To Generate Rules for Semantic Query Optimization. 18. Data Surveyor: Searching the Nuggets in Parallel. VII. KDD ApplicationsaEUR"19. Automating the Analysis and Cataloging of Sky Surveys. 20. Selecting and Reporting What is Interesting: The KEFIR Application to Healthcare Data. 21. Modeling Subjective Uncertainty in Image Annotation. 22. Predicting Equity Returns from Securities Data with Minimal Rule Generation. 23. From Data Mining to Knowledge Discovery: Current Challenges and Future Directions. VIII. AppendicesaEUR"A: Knowledge Discovery in Databases Terminology. B: Data Mining and Knowledge Discovery Internet Resources. About the Editors. Index. Printed Pages: 628. First edition, [Advances in Knowledge Discovery and Data MiningFayyd et al.9788120341340], PHI Learning 2010<

2010, ISBN: 9788120341340

PHI Learning 2010, New Softcover . This book brings together the latest researchaEUR"in statistics, databases, machine learning, and artificial intelligenceaEUR"that are part of the excit… Mehr…

PHI Learning 2010, New Softcover . This book brings together the latest researchaEUR"in statistics, databases, machine learning, and artificial intelligenceaEUR"that are part of the exciting and rapidly growing field of Knowledge Discovery and Data Mining. The chapters of this book, organized into eight sections, span fundamental issues of knowledge discovery, classification and clustering, trend and deviation analysis, dependency derivation, integrated discovery systems, augmented database systems, and application case studies: Part One deals with fundamental issues in discovery. Part Two examines specific techniques for data mining. Part Three presents methods for dealing with trend and deviation analysis. Part Four focuses on data mining techniques for deriving dependencies. Part Five discusses integrated discovery systems. Part Six presents approaches for next-generation database systems. Part Seven presents several real and successful applications. The Appendices provides a list of terms used in the literature of this fast-expanding field, and a list of online resources for the KDD researcher. CONTENTS: Foreword. Preface. 1. From Data Mining to Knowledge Discovery: An Overview. I. FoundationsaEUR"2. The Process of Knowledge Discovery in Databases: A Human-Centered Approach. 3. Graphical Models for Discovering Knowledge. 4. A Statistical Perspective on Knowledge Discovery in Databases. II. Classification and ClusteringaEUR"5. Inductive Logic Programming and Knowledge Discovery in Databases. 6. Bayesian Classification (AutoClass): Theory and Results. 7. Discovering Informative Patterns and Data Cleaning. 8. Transforming Rules and Trees into Comprehensible Knowledge Structures. III. Trend and Deviation Analysis: 9. Finding Patterns in Time Series: A Dynamic Programming Approach. 10. Explora: A Multipattern and Multistrategy Discovery Assistant. IV. Dependency DerivationaEUR"11. Bayesian Networks for Knowledge Discovery. 12. Fast Discovery of Association Rules. 13. From Contingency Tables to Various Forms of Knowledge in Databases. V. Integrated Discovery SystemsaEUR"14. Integrating Inductive and Deductive Reasoning for Data Mining. 15. Metaqueries for Data Mining. 16. Exploration of the Power of Attribute-Oriented Induction in Data Mining. VI. Next Generation Database SystemsaEUR"17. Using Inductive Learning To Generate Rules for Semantic Query Optimization. 18. Data Surveyor: Searching the Nuggets in Parallel. VII. KDD ApplicationsaEUR"19. Automating the Analysis and Cataloging of Sky Surveys. 20. Selecting and Reporting What is Interesting: The KEFIR Application to Healthcare Data. 21. Modeling Subjective Uncertainty in Image Annotation. 22. Predicting Equity Returns from Securities Data with Minimal Rule Generation. 23. From Data Mining to Knowledge Discovery: Current Challenges and Future Directions. VIII. AppendicesaEUR"A: Knowledge Discovery in Databases Terminology. B: Data Mining and Knowledge Discovery Internet Resources. About the Editors. Index. Printed Pages: 628. First edition, [NA]<

ISBN: 9788120341340

Brand New. "International Edition". ISBN number and front cover may be different in rare cases but contents are same as the US edition. Printed in black & white i… Mehr…

Brand New. "International Edition". ISBN number and front cover may be different in rare cases but contents are same as the US edition. Printed in black & white in English language. Territorial restrictions may be printed on the book. For expedited shipping, Get it fast within 3-5 business days by FEDEX/UPS/DHL with Tracking Number. Kindly provide day time phone number in order to ensure smooth delivery. No shipping to PO BOX, APO, FPO addresses.We may ship from Asian regions for inventory purpose.100% Customer satisfaction guaranteed!<