Instructors:

• Anna Monreale
  • KDDLab, Università di Pisa
  • anna [dot] monreale [at] unipi [dot] it
• Lorenzo Mannocci
  • KDDLab, Università di Pisa
  • lorenzo [dot] mannocci [at] di [dot] unipi [dot] it

[17-09-2026] Introduction Python Notebook

Hi everyone, I have uploaded a notebook on Didawiki covering the basics of Python for those who are not familiar with the programming language.

Lorenzo

[15-09-2026] Exam Modality and Project Groups

Hi everyone, I have created a shared Google Sheet to collect information about your exam modality and, for students choosing the Project, to organize the project groups.

🔗 Google Sheet: https://docs.google.com/spreadsheets/d/1qNdnhYP-SDP4sEpsreu2V1j-_SlrZRMdyG8jG4zepJA/edit?usp=sharing

⚠️ All students must fill in the Google Sheet, regardless of whether they choose the Project or the Written Exam.

Please fill in the following fields:

• ID: already pre-filled. Do not modify it.
• Surname, Name, Student ID, Email: enter your personal information.
• Curriculum: select your curriculum from the available options.
• Exam's modality: select one of the two available options:
• * Project
• * Written exam
• Group ID: fill in this field only if you choose the Project. Groups must consist of 3 students. All members of the same group must enter the same numerical Group ID. When creating a new group, use the next available number (e.g., if the last existing Group ID is 4, the new group should use 5).
• Note: optional; use it only if you need to communicate additional information.

📅 Deadline: September 29 By this date, all students must have completed the Google Sheet and selected their exam modality. Students choosing the Project must also have formed a group of 3 students and entered the corresponding Group ID. You are free to choose your group members. However, we encourage you to form groups with students from different curricula whenever possible, as different backgrounds can bring complementary knowledge and skills that can be useful when working on the project. Please make sure not to modify information entered by other students.

The Data Mining course tackles the analysis of large collections of data, and the extraction of information and patterns. It aims to explore core components of the Knowledge Discovery from Data (KDD) process, and focuses on:

• Data understanding
• Data cleaning, preparation, and transformation
• Data analysis: outlier detection and data representation
• Data clustering
• Anomaly detection
• Pattern extraction: itemset, rules, association rules, and sequential patterns
• Inference models: trees, and ensemble models
• Time Series
• Responsible data use: privacy and interpretability

Classes

Office hours - Ricevimento:

• Anna Monreale: TBD - Online using Teams or in my Office (Appointment by email).
• Lorenzo Mannocci: TBD

A https://teams.microsoft.com/l/team/19%3ATCuLDK4v7mUu1glOnSMQpceEjW9UYlClGxEwmjWq8Xs1%40thread.tacv2/conversations?groupId=a23b366e-ec68-4738-9104-f2321fe4e8e1&tenantId=c7456b31-a220-47f5-be52-473828670aa1 will be used ONLY to post news, Q&A, and other stuff related to the course. The lectures will be only in presence and will NOT be live-streamed.

Books

Online tutorials

Slides

The slides used in the course will be inserted in the calendar after each class. Some are part of the slides provided by the textbook's authors Slides per "Introduction to Data Mining".

• Exercises on Clustering: ex._clustering.pdf
• Some text of past exams of a similar course: 2017-1-19.pdf, 2017-9-6.pdf, 2016-05-30-dm1-seconda.pdf, dm2_exam.2017.06.13_solutions.pdf, dm2_exam.2017.07.04_solutions.pdf, dm2_mid-term_exam.2017.06.06_solutions.pdf
• Some exercises (partially with solutions) on sequential patterns and time series can be found in the following texts of exams from the last years: dm2_exam.2015.04.13.results.pdf, dm2_exam.2016.04.4_sol.pdf, dm2_exam.2016.04.5_sol.pdf, dm2_exam.2016.06.20_sol.pdf, dm2_exam.2016.07.08_sol.pdf
• Some very old exercises (part of them with solutions) are available here, most of them in Italian, not all of them on topics covered in this year program: Verifica 2006, Verifica 2005 (con soluzioni), Verifica 2004, Verifica 5 giugno 2007, Verifica 26 giugno 2007, Verifica 24 luglio 2007 (e Soluzioni), Verifica 18 luglio 2008 - parte 1, Verifica 18 luglio 2008 - parte 2, Exam with solution 2010-06-01,Exam with solution 2010-06-22, Exam with solution 2010-09-09, Exam with solution 2010-07-13

The exam can be taken in one of two ways:

Project track:

• Project to be delivered after the end of the course and discussed during the oral exam. Note that the project score will be finalized with the project discussion.
• Oral exam

During the course, you will have some “Project presentation” sessions wherein you’ll briefly (~3 minutes) present your work, and receive feedback from the lecturers. These sessions do not contribute to your grade.

Written test track

• Written exam: to be delivered during the standard exam sessions and can include both theoretical questions and exercises.
• Oral exam

Note that a passing grade for the project/written exam is required to be admitted to the oral exam.

Project Guidelines:

Project and Deadlines

Information about the dataset to be analyzed and project description:

• Dataset.
• Project description.
• Deadline.

• How to book for the exam colloquium? In https://esami.unipi.it/ you can find the dates for the exam: one for January and one for February. Each student must do the registration on one of the 2 dates. These are not the dates of the colloquium but we will use the list of registered students for organizing the exam dates. We will share with you a calendar for the oral exam.

Data Mining (309AA) - 9 CFU A.Y. 2025/2026

Data Mining (309AA) - 9 CFU A.Y. 2024/2025

Data Mining (309AA) - 9 CFU A.Y. 2023/2024

Data Mining (309AA) - 9 CFU A.Y. 2022/2023

Data Mining (309AA) - 9 CFU A.Y. 2021/2022

Data Mining (309AA) - 9 CFU A.Y. 2020/2021

DM-2019/20