DATA MINING AND KNOWLEDGE EXTRACTION
Stampa
Enrollment year
2021/2022
Academic year
2023/2024
Regulations
DM270
Academic discipline
INF/01 (COMPUTER SCIENCE)
Department
DEPARTMENT OF MATHEMATICS "FELICE CASORATI"
Course
ARTIFICIAL INTELLIGENCE
Curriculum
PERCORSO COMUNE
Year of study
Period
1st semester (02/10/2023 - 21/01/2024)
ECTS
6
Lesson hours
56 lesson hours
Language
English
Activity type
WRITTEN AND ORAL TEST
Teacher
CALAUTTI MARCO (titolare) - 6 ECTS
Prerequisites
Students should a basic understanding of algorithms and data structures and a very basic knowledge of statistics.
Learning outcomes
The goal of the course is to discuss the knowledge discovery in databases process, with a brief overview on data collection, data preprocessing, data transformation, and a more in-depth analysis of the data mining step.

Students will learn about different algorithms for extracting patterns and knowledge from different forms of data. Students will learn to distinguish the different kind of data, and how to convert between one form to the other.

Moreover, students will learn the main data mining tasks: Association pattern mining, clustering, outlier detection, and data stream mining, and will recognized which tasks are more suitable for each application.
Course contents
The course will touch on different topics regarding the data mining process:
- Introduction to data mining
- Data preparation and transformation
- Association pattern mining
- Cluster Analysis
- Outlier Detection
- Algorithms for data streams
Teaching methods
The course will be given in english with lectures using slides with the support of a whiteboard for the theoretical part. The practical part will be given using Python notebooks discussed live with students.
Reccomended or required readings
The main references for the course are:
- Data Mining: The Textbook (Charu C. Aggarwal)
- Introduction to Data Mining (Pang-Ning Tan, Michael Steinbach, Vipin Kumar)

Some parts of the course might be taken from:
- Mining of Massive Datasets (Jure Leskovec, Anand Rajaraman, Jeff Ullman)
Assessment methods
At the end of the course, students will be asked to present a project, where the problem specification, the motivation, the set of state-of-the-art techniques, the solution, the implementation description, and the experimental evaluation that were performed are described in a report, that will be then discussed during an oral exam.
Further information
Sustainable development goals - Agenda 2030