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Data Mining

Objectives

In terms of skills, this discipline aims to stimulate the student to:
 - the analysis and synthesis;
 - The organization and planning;
 - The writing and speaking in Portuguese ;
 - Problem solving , partially structured ;
 - The ability to make decisions ;
 - Teamwork ;
 - The ability to apply acquired knowledge in practice ;
 - The ability to generate new ideas ( creativity ) ;
 - Leadership ;
 - Work independently ;

General characterization

Code

100031

Credits

6.0

Responsible teacher

Roberto André Pereira Henriques

Hours

Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

Prerequisites

No special requirements

Bibliography

  • Dean Abbott (2014) “Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst”, Wiley
  • Daniel T. Larose, Chantal D. Larose (2015) “Data Mining and Predictive Analytics”, 2nd Edition, Wiley
  • Mitchell, T., (1997) “Machine Learning”, McGraw Hill.
  • Hand, D. J., Mannila, H., Smyth, P. (2001) “Principles of Data Mining (Adaptive Computation and Machine Learning)”, MIT Press.
  • Collica, R. (2011), “Customer Segmentation and Clustering Using SAS Enterprise Miner” 2nd Edition, SAS Publishing

Teaching method

Lectures were theory is presented
Practical classes in computer rooms allowing students to apply the presented concepts.
Tutorial classes in which students must work autonomously,

Evaluation method

1st term

  • Exam (65%)
  • Project 1 (15%)
  • Project 2 (15%)
  • 1 Practical handout (5%)

2nd term

  • Exam (70%)
  • Project 1 (15%)
  • Project 2 (15%)

Subject matter

  • Introduction to Data Mining;
  • Predictive and descriptive models;
  • Inductive learning;
  • Data Mining Methodology
    • The process;
    • The definition of the problem;
    • the quality measurement;
  • Data exploration;
  • Visualization tools;
  • Data pre-processing;
  • Descriptive Models;
    • Market basket analysis
    • RFM Analysis;
    • clustering algorithms
      • K-Means;
      • Self-Organizing Maps;
      • Additional topics on the segmentation;
  • Predictive Models
    • Simple classifiers
    • the Introduction to Bayesian classifiers
    • Instance-based classifiers
    • Classification Trees - DDT, Cart and C 4.5
    • Neural Networks - MLP

Additional Topics on Predictive Modelling

Programs

Programs where the course is taught: