Data Mining

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Data Mining

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Data mining and Data Warehousing

Data mining and Data Warehousing

  • 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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