Slide of the Data Mining and Text Mining course at the Politecnico di Milano for the MSc in Computer Science and Engineering.

1. Prof. Pier Luca Lanzi
Course Introduction
Data Mining andText Mining (UIC 583 @ Politecnico di Milano)

2. Prof. Pier Luca Lanzi
Data Mining and Text Mining
• Prof. Pier Luca Lanzi
Dipartimento di Elettronica, Informazione
e Bioingegneria
pierluca.lanzi@polimi.it
voice: 02 23993472
http://www.deib.polimi.it/people/lanzi
• Office Hours
Wednesday, from 14:30 until 16:00
2

3. Prof. Pier Luca Lanzi
Course Structure
• Introduction to basic Data Mining and Text Mining methods
(24 hours)
• Advaced Techniques and Applications
(16 hours)
• Final Project will involve an application to real-world data
3

4. Prof. Pier Luca Lanzi
Course Outline
• What is Data Mining?
• Data and knowledge representation
• Data exploration and preparation
• Data Mining tasks
§ Associations
§ Clustering
§ Classification
• Advanced techniques and applications
§ Text Mining
§ Graph Mining
§ Data Streams
4

5. Prof. Pier Luca Lanzi
syllabus

6. Prof. Pier Luca Lanzi

7. Prof. Pier Luca Lanzi
Course Material
• “Data Mining and Analysis: Fundamental Concepts and Algorithms,”
Mohammed Zaki and Wagner Meira Jr. Cambridge University Press in 2014.
http://www.dataminingbook.info
• “Mining of Massive Datasets Book,” by A. Rajaraman, J. Ullman.
http://www.mmds.org
• Course slides available on BEEP and articles distributed during the course
• Software
§ R Rstudio (http://www.rstudio.com)
§ Python/IPython (numpy, scipy, scikit, etc.)
§ BigML (http://www.bigml.com)
§ Rapid Miner/Weka (http://rapid-i.com/)
7

8. Prof. Pier Luca Lanzi
Additional Material
• “The Elements of Statistical Learning: Data Mining, Inference, and Prediction,”
Second Edition, February 2009, Trevor Hastie, Robert Tibshirani, Jerome
Friedman (http://statweb.stanford.edu/~tibs/ElemStatLearn/)
• “An Introduction to Data Science,” Jeffrey Stanton
https://ischool.syr.edu/media/documents/2012/3/DataScienceBook1_1.pdf
• Ian H. Witten, Eibe Frank. “Data Mining: Practical Machine Learning Tools and
Techniques with Java Implementations” 2nd Edition.
8

9. Prof. Pier Luca Lanzi
R Help Websites
• Quick-R
http://www.statmethods.net
• R Cookbook
http://www.cookbook-r.com
• R Bloggers
http://www.r-bloggers.com
• R on Stackoverflow
http://stackoverflow.com/tags/r/info
• Google R Styleguide
https://google-styleguide.googlecode.com/svn/trunk/Rguide.xml
9

10. Prof. Pier Luca Lanzihttp://www.kdnuggets.com/2012/08/poll-analytics-data-mining-programming-languages.html

11. Prof. Pier Luca Lanzi
http://xkcd.com/353/

12. Prof. Pier Luca Lanzi
Evaluation
• May 2015 First Midterm (15 points)
• June 2015 Second Midterm (18 points)
• July 2015 Full exam for those who failed midterms
12

13. Prof. Pier Luca Lanzi
Challenges and exercises might be proposed
during the course to substitute part of the written exam
There is also another way to pass the exam
http://www.kaggle.com
http://www.drivendata.org/
http://tunedit.org/data-competitions