CSC 478 Programming Data Mining Applications Course Summary Bamshad Mobasher DePaul University Bamshad Mobasher DePaul University

What we did  Data Mining Overview  The KDD Process  Data Preprocessing and Understanding  Using Python, Numpy, Pandas  Using Scikit-learn modules  Some emphasis on visualizing and understanding characteristics of the data  Supervised Knowledge Discovery  Classification  Regression Analysis  Techniques such as KNN, Ridge Regression, Decision Tree and Bayesian classification  Lots of emphasis on model evaluation  Evaluation metrics  Train-Test methodologies such as cross-validation  Systematic parameter selection (e.g., grid search) 2

What we did  Unsupervised Knowledge Discovery  Cluster analysis  Using PCA and SVD for dimensionality reduction, data characterization, and noise reduction.  Association rule discovery  Emphasis on using unsupervised approaches as components of larger knowledge discovery efforts  E.g., using PCA before clustering; using clustering as the basis for classification  Real application domains  Text Mining and document analysis/filtering  Recommender systems  Predictive modeling for marketing/business applications  Image analysis 3

What we did not do (and you should learn later)  Approaches for mining sequential/temporal data  Markov models; time series analysis, sequential pattern mining  More Ensemble and Hybrid Classifiers/Predictors  Combining multiple classifiers  Random Forest classifiers  Other Meta-learners such as Ada Boost  Support Vector Machines and Kernel-Based Classifiers  Topic modeling with Latent factor models  LDA  Latent Dirichlet Allocation 4