Instructor:

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

Teaching Assistant:

• Francesca Naretto
  • KDDLab, SNS, Pisa
  • francesca [dot] naretto [at] sns [dot] it
• * Lorenzo Mannocci
  • University of Pisa
  • lorenzo [dot] mannocci [at] phd [dot] unipi [dot] it

• [28.10.2022] The lectures on 16 and 17 November will be suppressed.
• [09.09.2022] he lectures will be only in presence and will NOT be live-streamed, but recordings of the lecture or of the previous years will be made available here for non-attending students.

• Fundamental concepts of data knowledge and discovery.
• Data understanding
• Data preparation
• Clustering
• Classification
• Pattern Mining and Association Rules
• Outlier Detection
• Time Series Analysis
• Sequential Pattern Mining
• Ethical Issues

Classes

Office hours - Ricevimento: Anna Monreale: Tuesday: 11:00-13:00 by online using Teams or at the Department of Computer Science, room 374/E (Please ask an appointment by email). Francesca Naretto: TDB

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, but recordings of the lecture or of the previous years will be made available here for non-attending students.

• Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining. Addison Wesley, ISBN 0-321-32136-7, 2006
  • http://www-users.cs.umn.edu/~kumar/dmbook/index.php
  • Chapters 4,6 and 8 are also available at the publisher's Web site.
• Berthold, M.R., Borgelt, C., Höppner, F., Klawonn, F. GUIDE TO INTELLIGENT DATA ANALYSIS. Springer Verlag, 1st Edition., 2010. ISBN 978-1-84882-259-7
• Laura Igual et al. Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications. 1st ed. 2017 Edition.
• Jake VanderPlas. Python Data Science Handbook: Essential Tools for Working with Data. 1st Edition.
• For Python Notions: Very basic notions on Python

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

• Python - Anaconda (at least 3.7 version!!!): Anaconda is the leading open data science platform powered by Python. Download page (the following libraries are already included)
• Scikit-learn: python library with tools for data mining and data analysis Documentation page
• Pandas: pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Documentation page

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 3 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

1. Dataset:Twitter Data
2. Deadline: the fist part has to be delivered within November 5th 2022 12, 2022. Send an email to: anna.monreale@unipi.it, francesca.naretto@sns.it, lorenzo.mannocci@phd.unipi.it

• Second part of the project consists in the assignment described here: Updated Project Description
  1. Deadline: Jan 8, 2023

• Third part of the project consists in the assignment described here: Complete Project Description

1. Note that the document contains also rules for the delivery and final exam!
2. Deadline: Jan 8, 2023

Students who did not deliver the above project within Jan 8, 2023 need to ask by email a new project to the teachers. The project that will be assigned will require about 2 weeks of work and after the delivery it will be discussed during the oral exam.

Paper Presentation (OPTIONAL)

Students need to present a research paper (made available by the teacher) during the last week of the course. This presentation is OPTIONAL: Students that decide to do the paper presentation can avoid the oral exam with open questions. They only need to present the project (see next point). The paper presentation can be done by the group or by a single person.

Oral Exam

• Project presentation (with slides) – 10-15 minutes: mandatory for all the students
• Open questions on the entire program: optional only for students opting for paper presentation.

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

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

DM-2019/20