Knowledge Discovery from Data Bases

Code:

CC5015

Acronym:

KDD

Level:

500

Keywords
Classification	Keyword
OFICIAL	Informatics

Instance: 2022/2023 - 1S

Active?	Yes
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Doctoral Program in Computer Science - MAP joint programme

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
PDMAPI	8	Official Study Plan since 2020/2021	1	-	6	30	162

Teaching language

English

Objectives

At the end of the semester the students should be able to:

Formulate a decision problem as a data mining problem;
Identify the basic tasks in knowledge discovery from data bases;
Identify and use the main methods in solving data mining problems;
Apply the main methods and algorithms for each mining task;
Apply the main methods and algorithms in real-world problems and adapt to new contexts

Learning outcomes and competences

Knowledge of how to formulate a problem as a problem of knowledge extraction. Ability to apply methods / algorithms to a new problem of data analysis, and evaluate the results and understand the functioning of the methods studied.

Working method

Presencial

Program

Introductory Concepts

– Introduction to Knowledge Discovery in Data Bases

∗ From OLAP to On-Line Analytical Mining;

∗ Data Mining tasks;

– Cluster Analysis

∗ Cluster Analysis: concepts and methods;

∗ Partitioning and Hierarchical Methods;

– Association Analysis

∗ Frequent pattern mining;

∗ Frequent Sequence mining;

– Predictive Data Mining: Classification and Regression.

∗ Optimization Methods: Artificial Neural Networks; Support Vector Machines.

∗ Probabilistic Methods: Bayesian Classifiers;

∗ Search based Methods: Decision Trees and Rules.

– Evaluation in Predictive Data Mining.

∗ Evaluation: goals and perspectives;

∗ Loss Functions and Cost-benefit analysis;

∗ Bias-Variance analysis;

– Ensembles and Multiple Models

∗ Concepts and methods;

∗ Combining Homogeneous Models;

∗ Combining Heterogeneous models;

Advanced Topics

– Social Network Analysis

∗ Concepts and methods;

∗ Evolution of Networks;

– Text Mining

∗ Concepts and methods;

∗ Information retrieval;

∗ Document classification;

– Web Mining and Link Analysis

∗ Concepts and methods;

∗ Web and Structure mining;

∗ Link analysis;

– Big Data and Data stream Mining

∗ Big Data: Applications and tools

∗ Concepts and methods;

∗ Summarizing data streams;

∗ Knowledge discovery from data streams;

– Data Mining Standards and Processes

Mandatory literature

J. Gama, A. Carvalho, K. Faceli, A. Lorena, M. Oliveira; Extração de Conhecimento de Dados - Data Mining, Sílabo, 2012
Jiawei Han e Micheline Kamber; Data Mining, Concepts and Techniques, Morgan Kaufmann, 2006
J. Gama; Knowledge Discovery from Data Streams, CRC Press, 2010

Teaching methods and learning activities

The teaching method consists of theoretical-practical classes.

Evaluation Type

Distributed evaluation without final exam

Assessment Components

designation	Weight (%)
Participação presencial	10,00
Trabalho prático ou de projeto	90,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Elaboração de projeto	28,00
Estudo autónomo	28,00
Frequência das aulas	28,00
Total:	84,00

Eligibility for exams

Submit assignment

Calculation formula of final grade

The evaluation consists of home-works.