CS 412 Introduction to Data Mining

Course Objectives

Be able to understand the key concepts of data mining techniques, including data preprocessing, data warehousing and cube, frequent pattern mining, classification, and clustering.
Be able to apply the key data mining techniques to realistic settings, evaluate and analyze the mining results.

Home Syllabus

Week	Topic
1	Introduction to Data Mining , Data Objects and Types, Central Tendency, Dispersion and Correlation, Normal Distribution
2	Hypothesis Testing, Visualization, Similarity Measures Part1
3	Similarity Measures Part 2, KL Divergence , Data Quality, Cleaning, and Integration
4	Data Reduction , Data Transformation, Dimensionality Reduction, Principal Component Analysis (PCA)
5	Classification Introduction, Decision Tree, Splitting Measures: Information Gain and Gini Index , Decision Tree Overfitting and Visualization
6	Bayes’ Theorem , Bayes’ Theorem Multiple Hypotheses, Naive Bayes Classifier , Linear Models
7	Logistic Regression, Evaluation, Model Selection, Classifier Evaluation and Statistical Significance of Model Selection, Lazy Learning, Ensemble Methods (1) Bagging and Random Forest, (2) Boosting
8	Bayesian Belief Networks, Support Vector Machines
9	Pattern Discovery Basics, Closed and Maximal Patterns, Pattern Discovery Methods: Apriori, FPGrowth , Apriori Improvements: Partitioning and Hashing, and ECLAT , Mining Closed Patterns
10	Sequential Pattern Mining, Evaluation
11	Constraint Pattern Mining, Graph Pattern Mining
12	Cluster Analysis Introduction , Partitioning-based Methods: K-Means, K-Means Initialization, K-Medoid , K-Medians and K-Modes, Kernel K-Means, Hierarchical Clustering: Basic Concepts
13	Density-based and Grid-based Clustering Methods, Probabilistic Model-based Clustering Methods, Validation
14	Deep Learning