Syllabus
1. Introduction to the area of data mining Motivation for data mining and its importance for practice, an overview of frequent data mining tasks, main data mining methodologies. Fundamental principles of database systems, data warehouses and the OLAP-technology (On-Line Analytical Processing), construction of data cubes, examples of data mining queries.
2. Fundamental paradigms of the data mining process Data gathering, preparation and preprocessing - sampling, variability and confidence, discretization of numeric attributes and handling nonnumerical variables, replacement of missing and empty values, series variables. Transformation, reduction and cleaning of the data - relationships among the attributes (hypothesis testing, correlation, regression, discriminant and cluster analysis). Main principles of machine learning - supervised training, self-organization, semi-supervised learning, training set, test set and validation set, generalization and overfitting, Occam´s razor. Validation of the obtained results - cross-validation, overall accuracy, confusion matrix, learning curve, lift curve, ROC curve, combination of models (bagging, boosting).
3. Techniques for association rule mining Market basket analysis - frequent itemsets, association rules, their formulation and main characteristics. Generation of frequent item combinations - algorithm apriori, "frequent-pattern-growth"-techniques (FP-Growth and TD-FP-Growth), combinational data analysis. Constraint-based search for interesting rules (specification of time, items, etc.).
4. Approaches to data classification and prediction Decision trees and their induction - algorithms ID3, C4.5, CART and CHAID. Bayessian models - Bayessian classifiers, Bayessian networks and techniques for their training and inference. Nature-inspired models - artificial neural networks of the perceptron type, SVM-machines, ELM-networks, genetic algorithms. Analogy based methods - instance-based learning, k-nearest neighbour classifiers, case-based reasoning.
5. Methods for cluster analysis The k-means algorithm, the choice of a suitable metric, evaluation of the obtained results (cluster validity), representation and visualization of the found clusters. Clustering based on the fuzzy set approach (FCM-clustering), neural approach and hierarchical clustering.
6. Social networks and their analysis Social networks - their representation and characteristics, SF-networks, link analysis and the algorithms PageRank and HITS. Applications - community discovery, evolution in social networks, link prediction and sentiment analysis.
Annotation
A rapid development in the area of data mining is motivated by the necessity to "translate" huge amounts of processed and stored data into meaningful information. This lecture is focused on understanding principal concepts and techniques applicable to data mining.
Basic principles of their application to novel solutions of practical tasks will be discussed in detail. These comprise mainly business and Web applications, but others as well.