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  1. 1. Introduction to Data Warehousing, A Multi-dimensional Data Model & Schemas,
  2. OLAP Operations & Servers (6 Lect.)
  3. • An overview and definition along with clear understanding of the four key-words
  4. appearing in the definition.
  5. • Differences between Operational Database Systems and Data Warehouses; Difference
  6. between OLTP & OLAP
  7. • Overview of Multi-dimensional Data Model, and the basic differentiation between "Fact"
  8. and "Dimension"; Multi-dimensional Cube
  9. • Concept Hierarchies of "Dimensions" Parameters: Examples and the advantages
  10. • Star, Snowflakes, and Fact Constellations Schemas for Multi-dimensional Databases
  11. • Measures: Their Categorization and Computation
  12. • Pre-computation of Cubes, Constraint on Storage Space, Possible Solutions
  13. • OLAP Operations in Multi-dimensional Data Model: Roll-up, Drill-down, Slice & Dice,
  14. Pivot (Rotate)
  15. • Indexing OLAP Data; Efficient Processing of OLAP Queries
  16. • Type of OLAP Servers: ROLAP versus MOLAP versus HOLAP
  17. • Metadata Repository
  18. 2. Data Warehouse Architecture; Further Development of Data Cube & OLAP
  19. Technology (3 Lect.)
  20.  
  21. • The Design of A Data Warehouse: A Business Analysis Framework; The Process of Data
  22. Warehouse Design
  23. • A 3-Tier Data Warehouse Architecture; Enterprise Warehouse, Data mart, Virtual
  24. Warehouse
  25. • Discovery-Driven Exploration of Data Cubes; Complex Aggregation at Multiple
  26. Granularity: Multi-feature Cubes
  27. • Constrained Gradient Analysis of Data Cubes
  28. 3. P re-processing (7 Lect.)
  29. • The need for Pre-processing, Descriptive Data Summarization
  30. • Data Cleaning: Missing Values, Noisy Data, Data Cleaning as a Process
  31. • Data Integration & Transformation
  32. • Data Cube Aggregation; Attribute Subset Selection
  33. • Dimesionality Reduction: Basic Concepts only
  34. • Numerosity Reduction: Regression & Log-linear Models, Histograms, Clustering,
  35. Sampling
  36. • Data Dicretization & Concept Hierarchy Generation
  37. • For Numerical Data: Binning, Histogram Analysis, Entropy-based Discretization,
  38. Interval Merging by x Analysis, Cluster Analysis, Discretization by Intuitive Partitioning
  39. • For Categorical Data
  40. 4. Data Mining: Introduction (4 Lect.)
  41. • An Overview; What is Data Mining; Data Mining - on What Kind of Data
  42. • Data Mining Functionalities - What Kind of Patterns Can be Mined; Concept/Class
  43. Description: Characterization & Discrimination; Mining Frequent Patterns, Associations,
  44. and Correlations; Classification & Prediction; Cluster Analysis; Outlier Analysis
  45. • Are All of the Patterns Interesting
  46. • Classification of Data Mining Systems
  47. • Data Mining Task Primitives
  48. • Integration of a Data Mining System with a Database or Data Warehouse System
  49. • Major Issues in Data Mining
  50.  
  51.  
  52. 5. Attribute-Oriented Induction: An Alternate Method for Data Generalization &
  53. Concept Description (4 Lect.)
  54. • Attribute-Oriented Induction for Data Characterization, and Its Efficient Implementation;
  55. Presentation of the Derived Generalization
  56. • Mining Class Comparisons: Discrimination between Different Classes
  57. • Class Descriptions: Presentation of both Characterization & Comparison
  58. 6. Mining Frequent Patterns, Associations, and Correlations (4 Lect.)
  59. • Basic Concepts: Market Basket Analysis; Frequent Itemsets, Closed Itemsets, and
  60. Association Rules; Frequent Pattern Mining: A Roadmap
  61. • Apriori Algorithm: Finding Frequent Itemsets Using Candidate Generation; Generating
  62. Association Rules from Frequent Itemsets; Improving the Efficiency of Apriori
  63. • From Association Mining to Correlation Analysis; Strong Rules Are Not Necessarily
  64. Interesting: An Example; From Association Analysis to Correlation Analysis
  65. 7. Classification & Prediction (9+2 Lect.)
  66.  
  67. • Introduction to Classification and Prediction; Basics of Supervised & Unsupervised
  68. Learning; Preparing the Data for Classification and Prediction; Comparing Classification
  69. and Prediction Methods
  70. • Classification by Decision Tree Induction, Attribute Selection Measures; Tree Pruning;
  71. Scalability and Decision Tree Induction
  72. • Rule-based Classification: Using IF-THEN Rules for Classification; Rule Extraction
  73. from a Decision Trees; Rule Induction Using a Sequential Covering Algorithm
  74. • Bayesian Classification: Bayes' Theorem, Naive Bayesian Classification; Bayesian
  75. Belief Networks
  76. • An Overview of Other Classification Methods (2 Lectures)
  77. • Prediction: Linear Regression; Non-linear Regression; Other Regression Models
  78. • Classifier Accuracy and Error Measures: Classifier Accuracy Measures; Predictor Error
  79. Measures
  80. • Evaluating the Accuracy of a Classifier or Predictor: Holdout Method and Random Sub-
  81. sampling; Cross Validation; Bootstrap
  82. • Ensemble Methods - Increasing the Accuracy: Bagging; Boosting
  83. 8. Cluster Analysis (6+2 Lect.)
  84. • Introduction to Cluster Analysis; Types of Data in Cluster Analysis; A Categorization of
  85. major Clustering Methods
  86. • Partitioning Methods; Centroid-Based Technique: K-Means Method; Overview of Other
  87. Clustering Methods
  88. • An Overview of Other Clustering Methods (2 Lectures)
  89. • Outlier Analysis; Statistical Distribution-based Outlier Detection; Distance-based Outlier
  90. Detection; Density-based Outlier Detection; Deviation-based Outlier Detection
  91. 9. Data Mining Applications (3 Lect.)
  92. • Data Mining for: (a) Financial Data Analysis; (b) The Retail Industry; (c) The
  93. Telecommunication Industry; (d) Biological Data Analysis; (e) Other Scientific
  94. Applications; (f) Intrusion detection
  95. • Data Mining Systems: (a) How to Choose; (b) Examples of Commercial Data Mining
  96. Systems
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