Data Mining

Academic year 2016–2017

"The success of companies like Google, Facebook, Amazon, and Netflix, not to mention Wall Street firms and industries from manufacturing and retail to healthcare, is increasingly driven by better tools for extracting meaning from very large quantities of data. 'Data Scientist' is now the hottest job title in Silicon Valley."      – Tim O'Reilly

 

The course will develop algorithms and statistical techniques for data analysis and mining, with emphasis on massive data sets such as large network data. It will cover the main theoretical and practical aspects behind data mining.

The goal of the course is twofold. First, it will present the main theory behind the analysis of data. Second, it will be hands-on and at the end students will become familiar with various state-of-the-art tools and techniques for analyzing data.

We will use Python for downloading data and implementing various algorithms using its rich libraries and Hadoop's MapReduce framework, Spark, Storm, Giraph, and Jubatus for mining of large-scale data.

 

Prerequisites

Students who wish to take this course should be familiar with Python programming and with the MapReduce framework.

 

Announcements

You can find more details about the project at the project web page.

The second homework is out; you can find it below. It is due on January 22.

The second homework is out; you can find it below. It is due on December 11.

The first homework is out; you can find it below. It is due on November 13. Read carefully the instructions about groups and about handing in.

 

 

Instructors

Aris Anagnostopoulos, Sapienza University of Rome.

Ioannis Chatzigiannakis, Sapienza University of Rome.

Aristides Gionis, Aalto University.

 

When and where:

Tuesday 14.00–17.15, Room A4

Thursday 15.45–17.15, Room A7

 

Office hours

You can use the office hours for any question regarding the class material, past or current homeworks, general questions on data mining, the meaning of life, pretty much anything. Send an email to the instructors for arrangement.

 

Syllabus

Chapters for which no book is mentioned refer to the "Mining of Massive Datasets" (see below). For the other textbooks, we refer to with the author initials: A, ZM, ZAL, MRS.

.

Date Topic Reading
September 27 Introduction to data mining Chapters 1.1, 1.3
Introduction to data mining
September 29 Introduction to probability A crash course on discrete probability
Check the background probability chapters below
October 4 Introduction to probability (cont.),
Similarity and distance measures
Chapter 3.5, Wikipedia on LCS, Slides
October 6 Similarity and distance measures (cont.) A Chapter 3.4
October 11 Text preprocessing and inverted indexes IIR book, chapters 1.0–1.4, 2.0–2.2, 4.0–4.2
October 13 Scoring and Term Weighting IIR book, chapters 6.2–6.3, 7.1.0
October 18 Information storage and processing for web search
Guest lecture by Nicola Tonellotto
Slides
October 20 Clustering Slides (thanks Evimaria!!)
October 25 Similarity search Slides
October 27 Locality-sensitive hashing (LSH) Slides
November 3 Shingling and minwise hashing Chapters 3.0–3.3
November 10 Data streaming Slides for probability and data streams.
November 10 Data streaming (cont.)
November 15 Introduction to graph mining Slides
November 17 Computing basic graph statistics Slides
November 22 Computing basic graph statistics (cont.), spectral graph analysis Slides
November 29 Spectral graph analysis (cont.)
December 1 Rank aggregation Slides
December 6 Recommender systems Slides

 

 

Homeworks

Check the "Examination format" section below for information about collaborating, being late, and so on.

Handing in: You must hand in the homeworks by the due date and time by an email to This email address is being protected from spambots. You need JavaScript enabled to view it. that will contain as attachment (not links!) a .zip or .tar.gz file with all your answers and subject

[Data Mining class] Homework #

where # is the homework number. After you submit, you will receive an acknowledgement email that your homework has been received and at what date and time. If you have not received an acknowledgement email within 1 day after you submit then contact the instructor.

The solutions for the theoretical exercises must contain your answers either typed up or hand written clearly and scanned.

The solutions for the programming assignments must contain the source code, instructions to run it, and the output generated (to the screen or to files).


We will not post the solutions online, but we will present them in class.

 

Project

You can find more information at the project web page.

 

Textbook and references

The main textbook is the "Mining of Massive Datasets," by J. Leskovec, A. Rajaraman, and J. D. Ullman. The printed version has been updated and you can download the latest version (currently 2.1) from the book's web site.

In addition, we will also use some chapters fro some other textbooks, all available online:

Finally, we will cover material from various sources, which we will post online as the course proceeds.

 

Python resources

The main programming language that we will use in the course is Python. There are currently two main versions of Python, version 2.7 and version 3.4, which are slightly incompatible, but it is easy to translate programs from one version to the other. Even though version 3.4 is newer and better designed, version 2.7 is still more widely used and most available libraries are written for it. Therefore, in the class we will use Python 2.7.

To learn the language you can find a lot of material online. You can start from Python's documentation site: http://docs.python.org/2.7.

If you would like to buy some books, you can check the

We will use several libraries in the class. The Anaconda distribution has packaged all of them together and you can download it for free.

If you have problems with Python installation you can obtain an ubuntu virtual machine with Python preinstalled. Contact the instructor for more information.

 

Notes, slides, and other material

Book chapters and notes:

1/10/2016: Background reading on combinatorics, basic probability, random variables, and basic probability distributions.

 

Examination format

The evaluation will consist of three parts:

  • 3 sets of homeworks
  • A final project. Details will be given during the course
  • A tutorial for some technology, which you have to perform in class to your classmates. Details will be given during the course.

In addition, we will take into account participation during class.