CSC 478 Programming Data Mining Applications Course Summary

1. CSC 478Programming Data Mining ApplicationsCourse Summary Bamshad Mobasher DePaul University

2. What we did • Data Mining Overview • The KDD Process • Data Preprocessing and Understanding • Using Python and Numpy • Using Scikit-learn modules • Some emphasis on visualizing and understanding characteristics of the data • Supervised Knowledge Discovery • Regression Analysis • Classification • 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

3. 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

4. What we did not do(and you should learn later) • Approaches for mining sequential/temporal data • Markov models; time series analysis, sequential pattern mining • Ensemble and Hybrid Classifiers/Predictors • Combining multiple classifiers • Random Forest classifiers • AdaBoost and meta-learners • Support Vector Machines and Kernel-Based Classifiers • Topic modeling with Latent factor models • LDA  Latent Dirichlet Allocation • Non-Negative Matrix Factorization