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Data Mining & Data Warehousing

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    This unique 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

    All topics not listed due to character limitations.

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