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.
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Slides and schedule available at Moodle.
- 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.
Study of data mining techniques, i.e., knowledge discovery from data (KDD). The KDD process includes the exploratory data analysis, preprocessing, identification of outliers, clustering, prediction, frequent patterns, and data warehouses G. James, D. Witten, T. Hastie, and R. Tibshirani, 2013, An Introduction to Statistical Learning: with Applications in R. 1 ed. Springer. J. Han, […]Continue reading →
Some lectures and materials to improve skills in writing articles in the computer field. Prof. Valtencir Zucolotto lectures. Continue reading →
Orientações gerais para alunos de mestrado Matricula trimestral A cada trimestre o aluno deve se inscrever em alguma atividade para não ter a matricula trancada. No quatro trimestre, o discente deve que não tiver defendido a qualificação, dever se inscrever em “Atividade de Estudo Integrado”. Se já tiver defendido a qualificação, deverá se inscrever em […]Continue reading →
Systems, architectures, algorithms, programming models, languages and software tools. Topics covered include parallelization and distribution models (MPI, Map-Reduce, etc.); Parallel architectures; Cluster and parallel and distributed computing systems, distributed and parallel algorithms, data structures and programming methodologies; applications; And performance analysis. Georg Hager and Gerhard Wellein. Introduction to High-Performance Computing for Scientists and Engineers. CRC Press, […]Continue reading →
I have developed some graphics guidelines to add students of preparing well-formatted graphics. The idea is to use GGplot2 in R, but trying to do it in a simple and easy way. I have created a Jupyter Notebook that presents some general graphics plots with an initial format that can be enhanced prior to the […]Continue reading →
Data Mining is the process of extracting knowledge from data. The main topics covered in this course include pre-processing, sorting, grouping, membership rules, anomaly, and the data mining process itself. The discipline aims to provide students with the fundamental skills needed to conduct their own research in data mining. References Mohammed J. Zaki and Wagner […]Continue reading →
The course aims to develop skills for the elaboration of articles and scientific projects in Computer Science. Thus, it is important that the student is aware of the importance of the main elements related to the research, since the choice of subject, problem definition, literature review, research execution to the writing process. Summary: (i) research […]Continue reading →
To introduce the students in scientific research activities compatible with the profile of the egress in computation, leading them to the experience of the main activities related to the conception, formalization, development, and evaluation of innovative computational models. Objective discipline: (i) presenting scientific problems in the context of a research project, (ii) stimulating the search […]Continue reading →
Study of data mining techniques, i.e., extraction of knowledge from large volumes of data. The knowledge extraction process includes exploratory data analysis, preprocessing, identification of outliers, grouping, classification, frequent patterns and data warehouses. Han, M. Kamber, and Pei J. Data Mining: Concepts and Techniques, Morgan Kaufmann Publisher, Burlington, MA, USA, 3rd Edition, 2011. Zaki, M.J. […]Continue reading →