(Executive Information Systems, Decision Support Systems, Statistics and Technical Data Analysis, Neural Networks, End-User Query and Reporting, Data Warehousing, Mapping and Visualization, Data Mining and OLAP, ).
• • Title • Data warehousing, data mining, and OLAP / Alex Berson, Stephen J. Also Titled • Data warehousing, data mining & OLAP Author • Berson, Alex. Other Authors • Smith, Stephen J. Published • New York: McGraw-Hill, c1997. Physical Description • xxvi, 612 p.: ill.; 25 cm.
Series • Subjects • • • Contents • Ch. Introduction to Data Warehousing • Ch. Client/Server Computing Model and Data Warehousing • Ch.
Parallel Processors and Cluster Systems • Ch. Distributed DBMS Implementations • Ch. Client/Server RDBMS Solutions • Ch.
Data Warehousing Components • Ch. Building a Data Warehouse • Ch. Mapping the Data Warehouse to a Multiprocessor Architecture • Ch. DBMS Schemas for Decision Support • Ch. Data Extraction, Cleanup, and Transformation Tools • Ch. Metadata • Ch. Reporting and Query Tools and Applications • Ch.
On-Line Analytical Processing (OLAP) • Ch. Patterns and Models • Ch. Statistics • Ch. Artificial Intelligence • Ch. Introduction to Data Mining • Ch.
Decision Trees • Ch. Neural Networks • Ch.
Nearest Neighbor and Clustering • Ch. Genetic Algorithms • Ch. Rule Induction • Ch. Selecting and Using the Right Technique • Ch.
Data Visualization. Putting It All Together • App. Big Data - Better Returns: Leveraging Your Hidden Data Assets to Improve ROI • App. Codd's 12 Guidelines for OLAP • App. 10 Mistakes for Data Warehousing Managers to Avoid. • Notes • Includes bibliographical references and index. Language • English ISBN •: Dewey Number • 005.74 Libraries Australia ID • Contributed by Get this edition.
× VitalSource eBook VitalSource Bookshelf gives you access to content when, where, and how you want. When you read an eBook on VitalSource Bookshelf, enjoy such features as: • Access online or offline, on mobile or desktop devices • Bookmarks, highlights and notes sync across all your devices • Smart study tools such as note sharing and subscription, review mode, and Microsoft OneNote integration • Search and navigate content across your entire Bookshelf library • Interactive notebook and read-aloud functionality • Look up additional information online by highlighting a word or phrase. • Dedication • Foreword • Foreword to Second Edition • Preface • Organization of the Book • To the Instructor • To the Student • To the Professional • Book Web Sites with Resources • Acknowledgments • Third Edition of the Book • Second Edition of the Book • First Edition of the Book • About the Authors • 1. Introduction • Publisher Summary • 1.1 Why Data Mining?
• 1.2 What Is Data Mining? • 1.3 What Kinds of Data Can Be Mined? • 1.4 What Kinds of Patterns Can Be Mined? • 1.5 Which Technologies Are Used? • 1.6 Which Kinds of Applications Are Targeted?
• 1.7 Major Issues in Data Mining • 1.8 Summary • 1.9 Exercises • 1.10 Bibliographic Notes • 2. Getting to Know Your Data • Publisher Summary • 2.1 Data Objects and Attribute Types • 2.2 Basic Statistical Descriptions of Data • 2.3 Data Visualization • 2.4 Measuring Data Similarity and Dissimilarity • 2.5 Summary • 2.6 Exercises • 2.7 Bibliographic Notes • 3. Batik mega mendung cirebon. Data Preprocessing • Publisher Summary • 3.1 Data Preprocessing: An Overview • 3.2 Data Cleaning • 3.3 Data Integration • 3.4 Data Reduction • 3.5 Data Transformation and Data Discretization • 3.6 Summary • 3.7 Exercises • 3.8 Bibliographic Notes • 4. Data Warehousing and Online Analytical Processing • Publisher Summary • 4.1 Data Warehouse: Basic Concepts • 4.2 Data Warehouse Modeling: Data Cube and OLAP • 4.3 Data Warehouse Design and Usage • 4.4 Data Warehouse Implementation • 4.5 Data Generalization by Attribute-Oriented Induction • 4.6 Summary • 4.7 Exercises • Bibliographic Notes • 5. Data Cube Technology • Publisher Summary • 5.1 Data Cube Computation: Preliminary Concepts • 5.2 Data Cube Computation Methods • 5.3 Processing Advanced Kinds of Queries by Exploring Cube Technology • 5.4 Multidimensional Data Analysis in Cube Space • 5.5 Summary • 5.6 Exercises • 5.7 Bibliographic Notes • 6. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods • Publisher Summary • 6.1 Basic Concepts • 6.2 Frequent Itemset Mining Methods • 6.3 Which Patterns Are Interesting?—Pattern Evaluation Methods • 6.4 Summary • 6.5 Exercises • 6.6 Bibliographic Notes • 7.
(Executive Information Systems, Decision Support Systems, Statistics and Technical Data Analysis, Neural Networks, End-User Query and Reporting, Data Warehousing, Mapping and Visualization, Data Mining and OLAP, ).
• • Title • Data warehousing, data mining, and OLAP / Alex Berson, Stephen J. Also Titled • Data warehousing, data mining & OLAP Author • Berson, Alex. Other Authors • Smith, Stephen J. Published • New York: McGraw-Hill, c1997. Physical Description • xxvi, 612 p.: ill.; 25 cm.
Series • Subjects • • • Contents • Ch. Introduction to Data Warehousing • Ch. Client/Server Computing Model and Data Warehousing • Ch.
Parallel Processors and Cluster Systems • Ch. Distributed DBMS Implementations • Ch. Client/Server RDBMS Solutions • Ch.
Data Warehousing Components • Ch. Building a Data Warehouse • Ch. Mapping the Data Warehouse to a Multiprocessor Architecture • Ch. DBMS Schemas for Decision Support • Ch. Data Extraction, Cleanup, and Transformation Tools • Ch. Metadata • Ch. Reporting and Query Tools and Applications • Ch.
On-Line Analytical Processing (OLAP) • Ch. Patterns and Models • Ch. Statistics • Ch. Artificial Intelligence • Ch. Introduction to Data Mining • Ch.
Decision Trees • Ch. Neural Networks • Ch.
Nearest Neighbor and Clustering • Ch. Genetic Algorithms • Ch. Rule Induction • Ch. Selecting and Using the Right Technique • Ch.
Data Visualization. Putting It All Together • App. Big Data - Better Returns: Leveraging Your Hidden Data Assets to Improve ROI • App. Codd's 12 Guidelines for OLAP • App. 10 Mistakes for Data Warehousing Managers to Avoid. • Notes • Includes bibliographical references and index. Language • English ISBN •: Dewey Number • 005.74 Libraries Australia ID • Contributed by Get this edition.
× VitalSource eBook VitalSource Bookshelf gives you access to content when, where, and how you want. When you read an eBook on VitalSource Bookshelf, enjoy such features as: • Access online or offline, on mobile or desktop devices • Bookmarks, highlights and notes sync across all your devices • Smart study tools such as note sharing and subscription, review mode, and Microsoft OneNote integration • Search and navigate content across your entire Bookshelf library • Interactive notebook and read-aloud functionality • Look up additional information online by highlighting a word or phrase. • Dedication • Foreword • Foreword to Second Edition • Preface • Organization of the Book • To the Instructor • To the Student • To the Professional • Book Web Sites with Resources • Acknowledgments • Third Edition of the Book • Second Edition of the Book • First Edition of the Book • About the Authors • 1. Introduction • Publisher Summary • 1.1 Why Data Mining?
• 1.2 What Is Data Mining? • 1.3 What Kinds of Data Can Be Mined? • 1.4 What Kinds of Patterns Can Be Mined? • 1.5 Which Technologies Are Used? • 1.6 Which Kinds of Applications Are Targeted?
• 1.7 Major Issues in Data Mining • 1.8 Summary • 1.9 Exercises • 1.10 Bibliographic Notes • 2. Getting to Know Your Data • Publisher Summary • 2.1 Data Objects and Attribute Types • 2.2 Basic Statistical Descriptions of Data • 2.3 Data Visualization • 2.4 Measuring Data Similarity and Dissimilarity • 2.5 Summary • 2.6 Exercises • 2.7 Bibliographic Notes • 3. Batik mega mendung cirebon. Data Preprocessing • Publisher Summary • 3.1 Data Preprocessing: An Overview • 3.2 Data Cleaning • 3.3 Data Integration • 3.4 Data Reduction • 3.5 Data Transformation and Data Discretization • 3.6 Summary • 3.7 Exercises • 3.8 Bibliographic Notes • 4. Data Warehousing and Online Analytical Processing • Publisher Summary • 4.1 Data Warehouse: Basic Concepts • 4.2 Data Warehouse Modeling: Data Cube and OLAP • 4.3 Data Warehouse Design and Usage • 4.4 Data Warehouse Implementation • 4.5 Data Generalization by Attribute-Oriented Induction • 4.6 Summary • 4.7 Exercises • Bibliographic Notes • 5. Data Cube Technology • Publisher Summary • 5.1 Data Cube Computation: Preliminary Concepts • 5.2 Data Cube Computation Methods • 5.3 Processing Advanced Kinds of Queries by Exploring Cube Technology • 5.4 Multidimensional Data Analysis in Cube Space • 5.5 Summary • 5.6 Exercises • 5.7 Bibliographic Notes • 6. Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods • Publisher Summary • 6.1 Basic Concepts • 6.2 Frequent Itemset Mining Methods • 6.3 Which Patterns Are Interesting?—Pattern Evaluation Methods • 6.4 Summary • 6.5 Exercises • 6.6 Bibliographic Notes • 7.