Study of data mining techniques, i.e., extraction of knowledge from large volumes of data. The knowledge extraction process includes exploratory analysis, data preprocessing, clustering, and prediction.

This short-course is regularly offered once a year at LNCC under the collaboration between CEFET/RJ and LNCC.

Fill this form to request access to the course.

Slides and schedule available at Moodle.

References:

- Han, M. Kamber, and Pei J. Data Mining: Concepts and Techniques, Morgan Kaufmann Publisher, Burlington, MA, USA, 3rd Edition, 2011.
- Zaki, M.J. and Jr., W.M. Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, Cambridge, United Kingdom, 1st Edition, 2014.
- Witten, I.H., Frank, E. and Hall M.A., Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Publishers, Burlington, MA, USA, 3rd Edition, 2011.
- Hastie, T., Tibshirani, R., Friedman, J., (2011), The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Publishing, New York, USA, 2 edition: 2013.
- James, G., Witten, D., Hastie, T., Tibshirani, R., (2013), An Introduction to Statistical Learning: with Applications in R., Springer Publishing, New York, USA, 1 edition: 2013.
- Lantz, B., (2013), Machine Learning with R. Packt Publishing Publishing, United Kingdom, 1st Edition, 2013.
- Leskovec, J., Rajaraman, A., Ullman, J.D., (2015), Mining of Massive Datasets. Cambridge University Press, Cambridge, United Kingdom, 2nd Edition, 2015.
- Shumway, R.H., Stoffer, D. S., (2010), Time Series Analysis and Its Applications: With Examples. Publisher Springer, New York, USA, 3 edition: 2010.