Data Mining

Syllabus

• Jiawei Han, Micheline Kamber. “Data Mining: Concepts and Techniques” Second Edition. Morgan Kauffman, 2006. (Amazon)
• Optional: Ian H. Witten, Eibe Frank. “Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations” 2nd Edition. (Amazon)

Exams

• 2006/2007 (zip)
• 2007/2008 (zip)
• 2008/2009 (zip)
• 2009/2010 (zip)
• 2010/2011 (zip))

Transparencies – Prof. Lanzi

• Course Introduction (pdf, video from the 2009 edition)
• Data Mining (pdf, video from the 2009 edition)
• Data Representation (pdf,video from the 2009 edition)
• Machine Learning for Data Mining (pdf, video from the 2009 edition)
• Classification
• Introduction (pdf, video from the 2009 edition)
• Decision Trees (pdf, video1, video2 from the 2009 edition)
• Rules (pdf, video from the 2009 edition)
• Naive Bayes & IBL (pdf, video from the 2009 edition)
• Ensembles (pdf)
• Evaluation (pdf, video from the 2009 edition)
• Association Rules
• Basics (pdf, video from the 2009 edition)
• Advanced Topics (pdf,video from the 2009 edition)
• Clustering
• Introduction (pdf, video from the 2009 edition)
• Partitioning Methods (pdf, video from the 2009 edition)
• Hierarchical Methods (pdf,video from the 2009 edition)
• Density-based and model-based methods (pdf, video from the 2009 edition)

Transparencies – Dr. Loiacono

• Support Vector Machines (pdf, SVM Applet)
• Text Mining (pdf)
• Web Mining (pdf)
• Data Exploration and Preprocessing (pdf)
• Graph Mining and Social Networks (pdf)
• Biological Data Analysis I (pdf)
• Biological Data Analysis II (pdf)
• Mining Data Streams (pdf)

Note: students enrolled in the Tecniche di Apprendimento Automatico course have to study only the topics included in Prof. Lanzi’s transparencies.