Data Preprocessing: data cleaning, integration, reduction, transformation and discretization.
Frequent pattern mining: basic concepts, A-priori algorithm, Pattern-Growth approach, vertical data format, pattern evaluation methods.
Classification: basic concepts, decision tree induction, Bayes classification methods, rule-based classification, lazy learners, techniques for improving accuracy, model evaluation and selection.
Clustering: basic concepts, partitioning methods, hierarchical methods, density-based methods, grid-based methods, model evaluation and selection, clustering with constraints.
Outlier detection: statistical, proximity-based, clustering-based and classification-based approaches.