Descriptive Data Mining


Data Mining uses interdisciplinary techniques, such as statistics, data visualization, database systems, and machine learning to identify original, useful, and understandable patterns in data.
This course will familiarize students with Data Mining applications and Data Mining projects lifecycle. Students will learn techniques for understanding and preparing data before building descriptive models, such as clustering or association rules (e.g., market basket analysis).

General characterization





Responsible teacher

Nuno Miguel da Conceição António


Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English


Familiarity with the main theme of the course is not required. But it is highly recommended that the students have knowledge of Inferential Statistics as well as good skills as a computer user.


Teaching method

The course is based on theoretical and practical classes. Several teaching strategies are applied, including slides presentation, step-by-step instructions on how to approach practical examples, and questions and answers. The practical component is oriented towards the exploration of the tools introduced to students (Microsoft Excel and SAS Enterprise Miner) and the development of the project.

Evaluation method

1 st Season: Exam (60%), Project (40%)
2 nd Season: Exam (60%), Project (40%)



  • Minimum grade in both the exam and the term project is 8.0 (out of 20)
  • Projects not submitted in Moodle until the deadline will be rejected

Subject matter

LU1. Introduction to Data Mining
LU2. Methodological aspects (KDD, SEMMA, CRISP-DM) LU3. Data visualization
LU4. Data understanding
LU5. Data preparation
LU6. Clustering
LU7. Self-Organizing maps
LU8. RFM model
LU9. Association rules and the Apriori algorithm
LU10. Data similarity and dissimilarity measures