Data Warehousing Data Mining And Olap Alex Berson Pdf Editor

× 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?

Magicsoft playout 5 2 10 138. Data-warehousing-data-mining-and-olap-data-warehousing-data-management.pdf - If you are searched for a ebook Data Warehousing, Data Mining, and OLAP (Data Warehousing/Data Management) by Alex Berson in pdf format, then you have. An Overview of Data Warehousing and OLAP Technology, Technical Report MSR-TR-97-14, Microsoft Research, 1997. 10 Web Mining: Information and Pattern Discovery on the World Wide Web, Proceedings of the 9th International Conference on Tools with Artificial Intelligence, p.558, November 03-08, 1997.

• 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. 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. Advanced Pattern Mining • Publisher Summary • 7.1 Pattern Mining: A Road Map • 7.2 Pattern Mining in Multilevel, Multidimensional Space • 7.3 Constraint-Based Frequent Pattern Mining • 7.4 Mining High-Dimensional Data and Colossal Patterns • 7.5 Mining Compressed or Approximate Patterns • 7.6 Pattern Exploration and Application • 7.7 Summary • 7.8 Exercises • 7.9 Bibliographic Notes • 8. Classification: Basic Concepts • Publisher Summary • 8.1 Basic Concepts • 8.2 Decision Tree Induction • 8.3 Bayes Classification Methods • 8.4 Rule-Based Classification • 8.5 Model Evaluation and Selection • 8.6 Techniques to Improve Classification Accuracy • 8.7 Summary • 8.8 Exercises • 8.9 Bibliographic Notes • 9.

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Classification: Advanced Methods • Publisher Summary • 9.1 Bayesian Belief Networks • 9.2 Classification by Backpropagation • 9.3 Support Vector Machines • 9.4 Classification Using Frequent Patterns • 9.5 Lazy Learners (or Learning from Your Neighbors) • 9.6 Other Classification Methods • 9.7 Additional Topics Regarding Classification • Summary • 9.9 Exercises • 9.10 Bibliographic Notes • 10. Cluster Analysis: Basic Concepts and Methods • Publisher Summary • 10.1 Cluster Analysis • 10.2 Partitioning Methods • 10.3 Hierarchical Methods • 10.4 Density-Based Methods • 10.5 Grid-Based Methods • 10.6 Evaluation of Clustering • 10.7 Summary • 10.8 Exercises • 10.9 Bibliographic Notes • 11. Advanced Cluster Analysis • Publisher Summary • 11.1 Probabilistic Model-Based Clustering • 11.2 Clustering High-Dimensional Data • 11.3 Clustering Graph and Network Data • 11.4 Clustering with Constraints • Summary • 11.6 Exercises • 11.7 Bibliographic Notes • 12. Outlier Detection • Publisher Summary • 12.1 Outliers and Outlier Analysis • 12.2 Outlier Detection Methods • 12.3 Statistical Approaches • 12.4 Proximity-Based Approaches • 12.5 Clustering-Based Approaches • 12.6 Classification-Based Approaches • 12.7 Mining Contextual and Collective Outliers • 12.8 Outlier Detection in High-Dimensional Data • 12.9 Summary • 12.10 Exercises • 12.11 Bibliographic Notes • 13. Data Mining Trends and Research Frontiers • Publisher Summary • 13.1 Mining Complex Data Types • 13.2 Other Methodologies of Data Mining • 13.3 Data Mining Applications • 13.4 Data Mining and Society • 13.5 Data Mining Trends • 13.6 Summary • 13.7 Exercises • 13.8 Bibliographic Notes • Bibliography • Index.