Coursera Illinois at Urbana-Champaign by Jiawei Han 2015.03.19 CONTENT 1. A brief Introduction to Data Mining 2. Pattern Discovery : Basic Concepts 3. Efficient Pattern Mining Methods 4. Pattern Evaluation 5. Mining Diverse Patterns 6. Constraint-Based Pattern Mining 7. Sequential Pattern Mining 8. Graph Pattern Mining 9. Pattern-Based Classification 10. Exploring Pattern Mining Applications Lecture 1 : A brief Introduction to Data Mining - We'are drowning in data but starving for knowledge ( a lot of data are unstructured ) - Data mining : a misnomer ! -> Knowledge mining from data - Extraction of interesting patterns (non-trivial, implicit, previously unknown and potentially useful) or knowledge from massive data. - Data mining is a interdisciplinary field (machine learning, pattern recognition, statistics, databases, big data, business intelligence..) Knowledge Discovery (KDD) Process Methodology View: Confluence of Multiple Disciplines Lecture 2 : Pattern Discovery : Ba