Data Analysis

Data Analysis
460-6016/01
čeština Data Analysis
460-6016/02
angličtina Data Analysis
460-6016/03
čeština Data Analysis
460-6016/04
angličtina

Summary

The content of the subject is following: data reduction methods, machine learning, data pre-processing, xxploratory data analysis, statistical data mining approach, cluster analysis (hierarchical and k-means clustering), Bayesian rules, k-nearest neighbor algorithm, decision trees, factor analysis , self-organizing SOM maps, association and fuzzy rules, rough sets, methods of analyzing multi-dimensional data, time series analysis, PCA, ICA, NMF, SVD, tensor data, tensor reduction, model evaluation, visualization, conceptual unions, knowledge mining from databases.

Literature

Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009.
Claudio Carpineto, Giovanni Romano. Concept Data Analysis: Theory and Applications,Wiley, 2004.

Advised literature

Bing Liu. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, Springer, 2009.
David Skillicorn. Understanding Complex Datasets: Data Mining with Matrix Decompositions, Chapman & Hall, 2007.
Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining, Addison Wesley, 2005.

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Language of instruction	čeština, angličtina, čeština, angličtina
Code	460-6016
Abbreviation	AD
Course title	Data Analysis
Coordinating department	Department of Computer Science
Course coordinator	prof. RNDr. Václav Snášel, CSc.