Data Mining
Academic year 2024–2025
"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
- Data Scientist: The Sexiest Job of the 21st Century
- Find true love with data mining
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 frameworks such as Spark, Storm, Giraph, and TensorFlow for mining of large-scale data.
Prerequisites
Students who wish to take this course should be familiar with Python programming.
Announcements
The fourth homework is out. It is due on December 29.
The third homework is out. It is due on December 1.
The second homework is out. It is due on November 17.
The first homework is out. It is due on November 3.
Make sure to register in the class mailing list. Email Aris to do so.
The first day of class is September 30.
Instructor
Aris Anagnostopoulos, Sapienza University of Rome.
Teaching Assistant (TA)
This email address is being protected from spambots. You need JavaScript enabled to view it. , Sapienza University of Rome.
When and where:
Monday 16.00–19.00, Room A5–A6
Wednesday 14.00–17.00, Room A5–A6
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 Gianluca or, if needed, to Aris for arrangement.
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 3) from the book's web site.
In addition, we will also use some chapters from some other textbooks, all available online:
- C. Aggarwal, "Data Mining: The Textbook," Springer (must be downloaded from Sapienza)
- M. J. Zaki and W. Meira, Jr., "Data Mining and Analysis: Fundamental Concepts and Algorithms," Cambridge University Press
- R. Zafarani, M. A. Abbasi, and H. Liu, "Social Media Mining: An Introduction," Cambridge University Press
- C. D. Manning, P. Raghavan, and H. Schütze, "Introduction to Information Retrieval," Cambridge University Press
- A. Blum, J. Hopcroft, and R. Kannan, "Foundations of Data Science," Cambridge University Press
The following book is not obligatory for the class, but is a vary useful book for the topic of feature engineering
- Pablo Duboue, "The Art of Feature Engineering," Cambridge University Press
For neural networks and GNNs, a nice book is
- Iddo Drori, "The Science of Deep Learning," Cambridge University Press
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 3.
To learn the language you can find a lot of material online. You can start from Python's documentation site: https://www.python.org/doc/.
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 Gianluca for more information.
Examination format
The evaluation will consist of two parts:
- 4 sets of homeworks
- A final project. Details will be given during the course
Late policy: Every homework must be returned by the due date. Homeworks that are late will lose 10% of the grade if they are up to 1 day (24h) late, 20% if they are 2 days late, 30% if they are 3 days late, and they will receive no credit if they are late for more than 3 days. However, you have a bonus of 3 late days, which you can distribute as you wish among all the homeworks. The homeworks will be discussed and graded at the end, during the final exam.
In addition, we will take into account participation during class.
Syllabus
Chapters for which no book is mentioned refer to the "Mining of Massive Datasets" (see above). For the other textbooks, we refer to with the author initials: A, ZM, ZAL, MRS, BHK.
| Date | Topic | Reading |
| September 30 | Introduction to data mining and data types, a crash course in probability | LRU Chapters 1.1, 1.3 Introduction to data mining, A crash course on discrete probability Check the background probability chapters below |
| October 2 | A crash course in probability (cont.) | |
| October 7 | Drash course in probability (cont.), similarity and distance measures |
Book chapter on quicksort
from the book of Mitzenmacher and Upfal on Probability and Computing Chapter 3.5 |
| October 14 | Preprocessing for text mining, the vector-space model, TF-IDF scoring, Inverted indexes | MRS Chapters 1.0–1.4, 2.0–2.2, 6.2, 7.1.0 |
| October 16 | Brief recap of Hadoop, MapReduce, and Spark; construction of large indexes. | LRU Chapters 3.4, 2.0–2.4, Quick introduction to MapReduce |
| October 21 | Lab on Apache SPARK | PySpark tutorial and slides by Fanilo Andrianasolo |
| October 23 | Shingles, minwise hashing | LRU Chapters 3.0–3.3 |
| October 28 | LSH, Introduction to clustering, hierarchical clustering | Chapters 3.4, 7.0–7.1.2, 7.2. | October 30 | k-means | Chapters 7.3.0–7.3.2. Chapter on k-means of the book of Christopher M. Bishop |
| November 4 | k-means++ | Paper by D. Arthur and S. Vassilvitskii | November 6 | Introduction to generative models, soft clustering, and expectation–maximization | Notes | November 11 | Principal component analysis, part 1 | Notes | November 13 | Principal component analysis, part 2 | Notes | November 18 | Principal component analysis, part 3 | Notes | November 20 | Some info on feature engineering |
| November 25 | Embeddings and Word2Vec | Original paper on Word2Vec |
| November 27 | PageRank and Personalized PageRank | LRU Chapters 5.1, 5.3 |
| December 2 | Node embeddings | Papers on DeepWalk and node2vec |
| December 4 | Graph neural networks, recommender systems | Book chapter on GNNs from the book of Drori The Science of Deep Learning, Slides on recommender systems |
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 the TA 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 2 days after the deadline then contact Gianluca.
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.
- Homework 1 (due: 3/11/2024, 23.59)
- Homework 2 (due: 17/11/2024, 23.59)
- Homework 3 (due: 1/12/2024, 23.59)
- Homework 4 (due: 29/12/2024, 23.59)
Notes, slides, and other material
Book chapters and notes:
Background reading on combinatorics, basic probability, random variables, and basic probability distributions.
Collaboration policy (read
carefully!): You can discuss with other students of the course
about the projects. However, you must understand well your solutions and
the final writeup must be yours and written in isolation. In addition,
even though you may discuss about how you could implement an algorithm,
what type of libraries to use, and so on, the final code must be yours.
You may also consult the internet for information, as long as it does
not reveal the solution. If a question asks you to design and implement
an algorithm for a problem, it's fine if you find information about how
to resolve a problem with character encoding, for example, but it is not
fine if you search for the code or the algorithm for the problem you are
being asked. For the projects, you can talk with other students of the
course about questions on the programming language, libraries, some API
issue, and so on, but both the solutions and the programming must be
yours.
If we find out that you have violated the policy and you have
copied in any way you will automatically fail. If you have any
doubts about whether something is allowed or not, ask the
instructor.
The same applies for generative AI tools, such
as ChatGPT. These can be useful tools in your work and there
are some homework questions in which we ask you explicitly to use them.
However, the use of such tools when it is not explicitly allowed
will be treated as plagiarism and is strictly prohibited.