CSE652: Knowledge Discovery and Data Mining

Syllabus



Contents

  • Overview of Data Mining
    • Definition, Original of Data Mining, Applications of Data Mining, Data Mining vs. OLAP and SQL
  • Data Preparation
    • Feature Ranking, Feature Discretization, Normalization, Outlier Detection Techniques
  • Classification
    • Classification Tree, Na├»ve Bayes, Neural Networks, k-NN Classifier, Support Vector Machines
  • Clustering
    • K-Means, Self-Organizing Map
  • Model Evaluation
    • Confusion Matrix, Recall and Precision, ROC Curve
  • Association
    • A-Priori Algorithm



Marks Distribution

  • Two Midterms - 30%
  • Final - 40%
  • Projects - 30%