Data mining & Data Warehousing
The app is a complete free handbook of Data mining & Data Warehousing which cover important topics, notes, materials on the course. This Data mining & Data Warehousing App lists 200 topics with detailed notes, diagrams, equations, formulas & course material, the topics are listed in 5 chapters. The app is must have for all the computer science & engineering students. The App is designed for quick learning, revisions, references at the time of exams and interviews. This app cover most of related topics and Detailed explanation with all the basics topics. Some of the topics Covered in the data warehousing and data mining app are: 1. Introduction to Data mining 2. Data Architecture 3. Data-Warehouses (DW) 4. Relational Databases 5. Transactional Databases 6. Advanced Data and Information Systems and Advanced Applications 7. Data Mining Functionalities 8. Classification of Data Mining Systems 9. Data Mining Task Primitives 10. Integration of a Data Mining System with a Data Warehouse System 11. Major Issues in Data Mining 12. Performance issues in Data Mining 13. Introduction to Data Preprocess 14. Descriptive Data Summarization 15. Measuring the Dispersion of Data 16. Graphic Displays of Basic Descriptive Data Summaries 17. Data Cleaning 18. Noisy Data 19. Data Cleaning Process 20. Data Integration and Transformation 21. Data Transformation 22. Data Reduction 23. Dimensionality Reduction 24. Numerosity Reduction 25. Clustering and Sampling 26. Data Discretization and Concept Hierarchy Generation 27. Concept Hierarchy Generation for Categorical Data 28. Introduction to Data warehouses 29. Differences between Operational Database Systems and Data Warehouses 30. A Multidimensional Data Model 31. A Multidimensional Data Model 32. Data Warehouse Architecture 33. The Process of Data Warehouse Design 34. A Three-Tier Data Warehouse Architecture 35. Data Warehouse Back-End Tools and Utilities 36. Types of OLAP Servers: ROLAP versus MOLAP versus HOLAP 37. Data Warehouse Implementation 38. Data Warehousing to Data Mining 39. On-Line Analytical Processing to On-Line Analytical Mining 40. Methods for Data Cube Computation 41. Multiway Array Aggregation for Full Cube Computation 42. Star-Cubing: Computing Iceberg Cubes Using a Dynamic Star-tree Structure 43. Pre-computing Shell Fragments for Fast High-Dimensional OLAP 44. Driven Exploration of Data Cubes 45. Complex Aggregation at Multiple Granularity: Multi feature Cubes 46. Attribute-Oriented Induction 47. Attribute-Oriented Induction for Data Characterization 48. Efficient Implementation of Attribute-Oriented Induction 49. Mining Class Comparisons: Discriminating between Different Classes 50. Frequent patterns 51. The Apriori Algorithm 52. Efficient and scalable frequently itemset mining methods All topics are not listed because of character limitations. Features : * Chapter wise complete Topics * Rich UI Layout * Comfortable Read Mode * Important Exam Topics * Very simple User Interface * Cover Most Of Topics * One click get related All Book * Mobile Optimized Content * Mobile Optimized Images This app will useful for quick reference. The revision of all concepts can be finished within Several hour using this app. Data mining & Data Warehousing is part of computer science, software engineering, AI, Machine learning & Statistical Computing education course and information technology & business management degree programs at various universities. Instead of giving us a lower rating, please mail us your queries, issues and give us valuable Rating And Suggestion So we can consider it for Future Updates. We will be happy to solve them for you.
