NOVA Information Management School

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

  1. Data Mining introduction
    Data Mining definition
    Data Mining uses and advantages
    Data Mining systems
  2. Data visualization
    Multidimensional data visualization techniques
  3. Data pre-processing
    Data summarization
    Data cleaning
    Integration and data transformation
    Data reduction
    Data discretization
  4. 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
  5. 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%).

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