Data Mining I
Code
200027
Academic unit
NOVA Information Management School
Credits
7.5
Teacher in charge
Fernando José Ferreira Lucas Bação
Teaching language
Portuguese. If there are Erasmus students, classes will be taught in English
Objectives
In terms of acquired knowledge, at the end of this unit the student must be able to:
- discuss the main DM topics;
- pre-process data;
- use different visualization tools to explore data;
- cluster data;
- organize and implement a clustering process;
- to describe the main algorithms used in the association analysis.
Prerequisites
Not applicable
Subject matter
- Data Mining introduction
Data Mining definition
Data Mining uses and advantages
Data Mining systems - Data visualization
Multidimensional data visualization techniques - Data pre-processing
Data summarization
Data cleaning
Integration and data transformation
Data reduction
Data discretization - Cluster analysis
Cluster analysis definition
Data types in the Cluster analysis
Partition methods
Hierarchical methods
Density-based methods
Grid-based methods
Model-based methods
Multidimensional data clustering
Outliers analysis - Patterns, associations and events
Basic concepts
Association rules
Association and correlation analysis
Association based on restrictions
Bibliography
Data Mining: Concepts and Techniques, Second Edition, Jiawei Han, Micheline Kamber, Jian Pei . The Morgan Kaufmann Series in Data Management Systems.; Introduction to Data Mining, Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Pearson Education, Inc., 2006.; 0; 0; 0
Teaching method
Not applicable
Evaluation method
The evaluation consists of an exam (50%) and a project (50%).