Instructors:
Classes
| Day of Week | Hour | Room |
|---|---|---|
| Monday | 09:00 - 11:00 | Room FIB PS4 |
| Wednesday | 14:00 - 16:00 | Room C |
| Friday | 11:00 - 13:00 | Room FIB PS4 |
Office hours - Ricevimento: Anna Monreale: Thu 09:00-11:00 - Online using Teams or in my Office (Appointment by email). Francesca Naretto: Mon 11:00-13:00 - Online using Teams or in my Office (Appointment by email).
A Teams Channel 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.
| Day | Topic | Learning material | References | Video Lectures | Teacher | |
|---|---|---|---|---|---|---|
| 1. | 16.09 | Overview. Introduction to KDD + Data Types | Overview Introduction to DADH Data Understanding | Chap. 1 Kumar Book | Monreale | |
| 2. | 18.09 | Data Understanding for tabular data | Slides of DU of the previous lecture | Chap.2 Kumar Book and additioanl resource of Kumar Book: Data Exploration Chap. If you have the first ed. of KUMAR this is the Chap 3 | Monreale | |
| 3. | 20.09 | Data Preparation for tabula Data | 3-data_preparation_dad.pdf | Chap.2 Kumar Book and additioanl resource of Kumar Book: Data Exploration Chap. If you have the first ed. of KUMAR this is the Chap 3 | Monreale | |
| 4. | 23.09 | Data Understanding and Preparation for images | 4-data-understanding_images.pdf | Digital Image processing, 3 edition, Rafael Gonzalez, Richard Woods | Naretto | |
| 5. | 25.09 | Data Understanding and Preparation for images and Time Series | 5-data-understanding_ts.pdf | Naretto | ||
| 6. | 27.09 | Data Understanding and Preparation for Time Series + Python Lab. | Intro to Python | Naretto | ||
| 7. | 30.09 | Data Understanding and Preparation for Tabular Python Lab. | data_und.zip | Naretto | ||
| 8. | 02.10 | Data Understanding and Preparation for Images and Time Series Python Lab. | data_und.zip | Naretto | ||
| 9. | 04.10 | Data Management and Data Warehousing | 6-dw.pdf | Monreale | ||
| 9. | 07.10 | Data Management and Data Warehousing | 6-dw.pdf | Monreale | ||
| 9. | 09.10 | Data Reporting - Project presentation | 6-dw.pdf | Monreale | ||
| 10. | 14.10 | Clustering: intro and k-means | 6-basic_cluster_analysis-intro.pdf 6-basic_cluster_analysis-kmeans.pdf | Chapter 7, Introduction to Data Mining, 2nd Edition by Tan, Steinbach, Karpatne, Kumar | Naretto | |
| 11. | 16.10 | Clustering: k-means and hierarchical | 7.basic_cluster_analysis-hierarchical.pdf | Chapter 7, Introduction to Data Mining, 2nd Edition by Tan, Steinbach, Karpatne, Kumar | Naretto | |
| 12. | 21.10 | Clustering: k-means variants and density Based approaches | 11-basic_cluster_analysis-kmeans-variants.pdf 10-basic_cluster_analysis-dbscan.pdf | Chapter 7, Introduction to Data Mining, 2nd Edition by Tan, Steinbach, Karpatne, Kumar | Monreale | |
| 13. | 23.10 | Clustering: Validity | 12-basic_cluster_analysis-validity.pdf | Chapter 7, Introduction to Data Mining, 2nd Edition by Tan, Steinbach, Karpatne, Kumar | Monreale | |
| 14. | 25.10 | Clustering and similarity for Images | 3.2-clustering_images.pdf | Naretto | ||
| 15. | 28.10 | Clustering and similarity for Time Series | 8_time_series_similarity_2024.pdf | Time Series Analysis and Its Applications. Robert H. Shumway and David S. Stoffer. 4th edition | Naretto | |
| 16. | 30.10 | Python Lab: Clustering | clustering_diabetes.zip images_similarity.zip timeseries_similarity_clustering.zip clustering_tabular_tips.zip | Naretto | ||
| 17. | 04.11 | Python Lab: Clustering + Frequent Pattern Mining | 17_association_analysis.pdf | Naretto, Monreale | ||
| 18. | 06.11 | Frequent Pattern Mining | same slides as previous lecture | Monreale | ||
| 19. | 08.11 | Sequential Pattern Mining | 18_sequential_patterns_2024.pdf | Monreale | ||
| 20. | 11.11 | Python lab: FPM + SPM | ar_spm.zip | Naretto | ||
| 21. | 13.11 | Classification for tabular | Naretto | |||
| 22. | 15.11 | Classification for tabular | 10-knn.pdf | Naretto | ||
| 23. | 18.11 | Classification for tabular | 10-lg.pdf | Naretto | ||
| 24. | 20.11 | Project | Monreale, Naretto | |||
| 25. | 22.11 | Classification for tabular | 10-rule-based-classifiers.pdf 11_2021-naive_bayes.pdf | Naretto | ||
| 26. | 25.11 | Classification for tabular | 13_ensemble_2023.pdf | Naretto |
Project
A project consists in data analyses based on the use of data mining tools. The project has to be performed by a team of 2 students. It has to be performed by using Python. The guidelines require to address specific tasks. Results must be reported in a unique paper. The total length of this paper must be max 25 pages of text including figures. The students must deliver both: paper (single column) and well commented Python Notebooks.
* First part of the project consists in the assignments described here: Project Description: DU and Clustering
Students who did not deliver the above project within Dec 31, 2024 need to ask by email a new project to the teachers. The project that will be assigned will require about 20 days of work and after the delivery it will be discussed during the oral exam.
Oral Exam
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 or project delivery but we will use the list of registered students for organizing the exam dates. After that deadline we will share with you a calendar for the oral exam.