Data Mining II

Code:

2MDA06

Acronym:

ECD II

Keywords
Classification	Keyword
OFICIAL	Information Technology

Instance: 2022/2023 - 2S

Active?	Yes
Web Page:	http://moodle.up.pt/course/view.php?id=1962
Course/CS Responsible:	Master in Modeling, Data Analysis and Decision Support Systems

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MADSAD	31	Official Syllabus - after 2020-2021	1	-	6	42	162

Teaching language

English

Objectives

At the end of the semester students should have the knowledge of various Data Mining tasks, the main methods and algorithms for each task, be able to apply these methods to new specific data analysis problems and have the capacity to evaluate, apply a critical posture in relation to results.

Learning outcomes and competences

Knowledge of various Data Mining tasks, the main methods and algorithms for each task; capacity to apply these methods to new specific data analysis problems and to evaluate and adopt a critical posture in relation to results.

Working method

Presencial

Program

Text Mining & Web Mining. Metalearning. Analysis of spatio-temporal data Data streams: classification, clustering, change detection. Social network analysis.

Mandatory literature

João Gama, A. Carvalho, K. Faceli, A. Lorena, M. Oliveira; Extração de Conhecimento de Dados - Data Mining, Edições Silabo
Jiawei Han; Data Mining: Concepts and Techniques, Morgan Kaufman, 2006
Joao Gama; Knowledge Discovery From Data Streams, CRC Press, 2009
P. Brazdil, C. Giraud-Carrier, C. Soares and R. Vilalta; Metalearning – Applications to Data Mining, 2009. ISBN: 978-3-540-73262-4
Charu Aggarwal; Data Streams - Models and Algorithms, Springer, 2007

Comments from the literature

  

Teaching methods and learning activities

Theretical-practical classes

Software

R
Weka
Knime

keywords

Physical sciences > Computer science > Modelling tools
Technological sciences > Technology > Knowledge technology

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Trabalho escrito	50,00
Trabalho laboratorial	25,00
Trabalho prático ou de projeto	25,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Apresentação/discussão de um trabalho científico	30,00
Trabalho laboratorial	20,00
Trabalho de investigação	20,00
Estudo autónomo	50,00
Frequência das aulas	42,00
Total:	162,00

Eligibility for exams

Home Hork 1 (Bayesian/Social Networks) -25%
Home Work 2 (Text Mining) - 25%
Home work 3 (report) - 50%

Calculation formula of final grade

The grade is an arithmetic mean of individual practical works.

Classification improvement

It is possible to submit the second version of one practical assignment under the following conditions:

The grade of the second version of the assignment cannot exceed the grade of the first version by more than two values.