Download Data Miing and Knowledge Discvoery

CSC 478
Programming Data Mining Applications
Course Summary
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)
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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
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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
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