Data mining and Data Warehousing

Features

• Description
• This unique free application is for all students of Data Mining & Data Warehousing across the world. It covers 200 topics of Data Mining & Data Warehousing in detail. These 200 topics are divided in 5 units.
• Each topic is around 600 words and is complete with diagrams, equations and other forms of graphical representations along with simple text explaining the concept in detail.
• The USP of this application is "ultra-portability". Students can access the content on-the-go from any where they like.
• Basically, each topic is like a detailed flash card and will make the lives of students simpler and easier.
• Some of topics Covered in this application are:
• 1. Introduction to Data mining
• 2. Data Architecture
• 3. Data-Warehouses
• 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 DataWarehouse 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
• 53. Mining Frequent Itemsets Using Vertical Data Format
• 54. Mining Multilevel Association Rules
• 55. Mining Multidimensional Association Rules
• 56. Mining Quantitative Association Rules
• 57. Association Mining to Correlation Analysis
• 58. Constraint-Based Association Mining
• 59. Introduction to classification and prediction
• 60. Preparing the Data for Classification and Prediction
• 61. Comparing Classification and Prediction Methods
• 62. Classification by Decision Tree Induction
• 63. Decision Tree Induction
• 64. Tree Pruning
• 65. Scalability and Decision Tree Induction
• 66. Bayesian Classification
• 67. Naive Bayesian Classification
• 68. Bayesian Belief Networks
• 69. Training Bayesian Belief Networks
• 70. Using IF-THEN Rules for Classification
• 71. Rule Extraction from a Decision Tree
• 72. Rule Induction Using a Sequential Covering Algorithm
• 73. Rule Pruning
• 74. Introduction to Back propagation
• 75. A Multilayer Feed-Forward Neural Network
• 76. Defining a Network Topology
• 77. Support Vector Machines
• 78. Associative Classification: Classification by Association Rule Analysis
• 79. Evaluating the Accuracy of a Classifier or Predictor

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