EIN 6905 – Data Mining

EIN 6905 – Data Analysis and Data Mining in Systems Engineering
Spring 2013 (3 credits)
Instructor: P.M. Pardalos, http://www.ise.ufl.edu/pardalos
Course description:
  1. Introduction to Data Representation and Mining.
  2. Statistical Methods.
  3. Clustering by k-means.
  4. k-nearest Neighbor Classification
  5. Artificial Neural Networks.
  6. Support Vector Machines.
  7. Biclustering.
  8. Validation Methods .
  9. Applications in Matlab.
  10. Robust Data Mining
  11. Massive Data Sets and Future Challenges.
Prerequisites: Graduate Students with DMOR (or equivalent) or advanced undergraduates with Matrix
Computations ESI 4327C, Statistics STA 4322, and Operations Research ESI 4312
Course material: The lectures will be based on several sources including the following books:
1. J. Abello, P.M. Pardalos, and M. Resende (Eds) , Handbook of Massive Data Sets, Kluwer
Academic Publishers (2002).
2. P.M. Pardalos and P. Hansen (Eds), Data Mining and Mathematical Programming, American
Mathematical Society (2008).
3. O. Seref, E. Kundakcioglu, P.M. Pardalos (Eds), Data Mining, Systems Analysis and Optimization
in Biomedicine, Springer (2008).
4. P.M. Pardalos, V. Boginski, A. Vazacopoulos (Eds), Data Mining in Biomedicine, Springer (2007).
5. A. Mucherino, P. Papajorgji, P.M. Pardalos, Data Mining in Agriculture, Springer (2009).
6. P. Xanthopoulos, P.M. Pardalos, T.B. Trafalis, Robust Data Mining, Springer (2013)
Grading: Grading will be based on homework 20%, three in class exams 60%, and project 20%.