Data classification and clustering techniques

Objectives

The objectives expressed in learning outcomes can be described as follows:

  • Summarize high-dimensional datasets;
  • Interpret qualitative and quantitative data;
  • Understand the main classification techniques;
  • Understand the main data clustering techniques;
  • Select the most appropriate analysis techniques for practical problems;
  • Use specific computational tools for data classification and clustering

Program

Regression Techniques:

  • Simple and multiple linear regression
  • Non-linear regression
  • Logistic regression Classification Techniques
  • Performance measures
  • ROC analysis Data grouping techniques
  • Principal component analysis
  • Clustering validity indices
  • Clustering similarity measures
  • Clustering algorithms Application of concepts and development of application examples through the use of the computational tool (R or Matlab).

Bibliography

  • Zelterman, D. (2015). Applied multivariate statistics with R. Cham: Springer.
  • Matloff, N. (2017). Statistical regression and classification: from linear models to machine learning. CRC Press.
  • Izenman, A. J. (2008). Modern multivariate statistical techniques. Regression, classification and manifold learning, 10, Springer-Verlag New York.
  • Batóg, J., Jajuga, K., & Walesiak, M. (Eds.). (2020). Classification and Data Analysis: Theory and Applications. Springer Nature.
  • Aggarwal, C. C., & Reddy, C. K. (2014). Data clustering. Algorithms and applications. Chapman&Hall/CRC Data mining and Knowledge Discovery series, Londra.

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