ALGORITHMIC METHODS OF DATA MINING AND LABORATORY

Obiettivi formativi

The course presents the main algorithmic techniques of data mining, necessary for data science. They offer to the student the basis for analyzing data for a variety of applications that deal with semistructured or unstructured data, such as textual data, transactions, and graph and information-network data. At the end of the course the student will have a knowledge of the main theoretical ideas of data mining, as well as some basic knowledge and experience in using programming tools for analyzing and mining data.

Canale 1
ARISTIDIS ANAGNOSTOPOULOS Scheda docente

Programmi - Frequenza - Esami

Programma
- Text mining - Basic algorithms for mining - Basic structures for data mining - Clustering - Graph mining
Prerequisiti
Prior programming knowledge Knowledge in probability
Testi di riferimento
J. Leskovec, A. Rajaraman, and J. Ullman, "Mining of Massive Datasets," Cambridge University Press 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
Frequenza
Classes are in person.
Modalità di esame
Choice of: - Homework, hackathon, and written exam - Written exam Probably also oral exam
Bibliografia
J. Leskovec, A. Rajaraman, and J. Ullman, "Mining of Massive Datasets," Cambridge University Press 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
Modalità di erogazione
The course is based on in-class theoretical lectures and an in-class hands on laboratory.
LUCA BECCHETTI Scheda docente

Programmi - Frequenza - Esami

Programma
- Text mining - Basic algorithms for mining - Basic structures for data mining - Clustering - Graph mining
Prerequisiti
Prior programming knowledge Knowledge in probability
Testi di riferimento
J. Leskovec, A. Rajaraman, and J. Ullman, "Mining of Massive Datasets," Cambridge University Press 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
Frequenza
Classes are in person.
Modalità di esame
Choice of: - Homework, hackathon, and written exam - Written exam Probably also oral exam
Modalità di erogazione
The course is based on in-class theoretical lectures and an in-class hands on laboratory.
  • Codice insegnamento1047221
  • Anno accademico2025/2026
  • CorsoData Science
  • CurriculumCurriculum unico
  • Anno1º anno
  • Semestre1º semestre
  • SSDING-INF/05
  • CFU9