Data Warehousing and Data Mining syllabus

Data Warehousing and Data Mining

• 1 Introduction (6 LHs)

  • Data mining and knowledge discovery
  • Data types, similarity and distance
  • Cleaning, integration, transformation and dimensionality reduction
• 2 Data Warehousing and Online Analytical Processing (8 LHs)

  • Warehouse schemas and measures
  • OLAP operations and data cubes
  • Cube computation
• 3 Pattern Mining (5 LHs)

  • Frequent and closed itemsets; association rules
  • Apriori and pattern growth
  • Rule generation and evaluation
• 4 Classification (12 LHs)

  • Decision-tree induction
  • Bayesian and lazy learning
  • Linear classifiers
  • Model selection and accuracy improvement
• 5 Cluster Analysis (10 LHs)

  • k-means, k-medoids and k-modes
  • Agglomerative and divisive methods
  • DBSCAN
  • Clustering evaluation
• 6 Outlier Detection (7 LHs)

  • Statistical and proximity approaches
  • Reconstruction-, clustering- and classification-based methods
• 7 Laboratory Works

  • Data warehouse implementation
  • Pattern mining, classification, clustering and outlier algorithms
  • Visualization tools