Data Mining II
Keywords |
Classification |
Keyword |
OFICIAL |
Computer Science |
Instance: 2018/2019 - 2S
Cycles of Study/Courses
Teaching language
Suitable for English-speaking students
Objectives
Identification and application of data mining techniques to extract knowledge from different data sources (e.g. text, web).
Learning outcomes and competences
The student is able to:
- recognize different problems solvable through the use of data mining techniques discussed and detailed in content;
- identify and specify data mining tasks similar to those discussed;
- obtain and pre-process data for the algorithms and tasks addressed;
- understand and use data mining algorithms;
- obtain, interpret, evaluate and use data mining models;
- Implement some of the algorithms and propose changes to improve them.
Working method
Presencial
Program
- Association Pattern Mining: frequent itemsets and association rules; Apriori algorithm; itemsets summarization and rules selection; FP-Growth algorithm. - Sequential Pattern Mining: GSP algorithm; PrefixSpan algorithm.- Web Mining: information retrieval; recommender systems; link analysis.- Text Mining: document clustering; document classification.- Outlier Mining: challenges; unsupervised, semi-supervised and supervised
techniques.Mandatory literature
Liu Bing 1963-;
Web data mining. ISBN: 978-3-642-19459-7
Hand David 1950-;
Principles of data mining. ISBN: 978-0-262-08290-7
Complementary Bibliography
Charu C. Aggarwal; Data Mining - The Texbook, Springer, 2015. ISBN: 978-3-319-14141-1
Teaching methods and learning activities
Theoretical-practical classes where the themes covered in the program will be exposed and some practical examples of application will be provided.
Software
RStudio
R
Evaluation Type
Distributed evaluation with final exam
Assessment Components
designation |
Weight (%) |
Exame |
70,00 |
Trabalho prático ou de projeto |
30,00 |
Total: |
100,00 |
Amount of time allocated to each course unit
designation |
Time (hours) |
Elaboração de projeto |
0,00 |
Estudo autónomo |
0,00 |
Frequência das aulas |
0,00 |
Total: |
0,00 |
Eligibility for exams
In order to be eligible for exams, students must: participate either in the two theoretical tests provided in the curricular unit or in the final exam consisting of two parts concerning the contents of test 1 and test 2; have a minimum grade the practical assignment of 35% (7 out of 20).
Calculation formula of final grade
The assessment of the unit is distributed and it is composed of two (2) theoretical tests and one (1) practical assignment.
The final grade is given as a weighted average of the practical grade and the theoretical grades using the following formula:
GFinal = 0.70 * GT + 0.30 * GP
where,
GT is the average of the grades on the two theoretical tests or of the final exams and
GP is given by the grade of the practical assignment
There will be 2 tests during the semester. These are not compulsory, but everyone that obtains minimum grade in each test, and in each practical assignment, and whose final grade (GFinal) is positive (above 9.5), does not need to go to the final exam.
Minimum grade of the tests and the practical assignment: 35% (7 out of 20)
Examinations or Special Assignments
The theoretical tests will take place in two theoretical classes, one in the middle of the semester and the other at the end of it.
The practical assignment will be announced during the semester and should be completed by at the end of the semester on a date to be announced.
Classification improvement
The evaluation of the practical assignment is not subject to improvement. The student can improve the theoretical grade by the two final exams.
Observations
All the provided material (slides, recommended books, etc.) is in the English language.