Description:
Teaching is performed by means of lectures and practices. Lectures are devoted to the theory of data mining. Practices are conducted in the computer class, where implementation of main algorithms is discussed using “R” language. The course consists of two parts. First part is devoted to the main notions and problems of data mining. Main notions and concept, such as, distance function, cluster analysis, classification, outlier analysis, associative pattern mining are covered. The second part of the course is devoted to the application of this knowledge to such problems as: spatial data mining, stream data mining, graph data mining and social networks analysis.
Learning outcomes
The student:
Is familiar with main notions used in datamining such as Attribute, feature, distance/ similarity function.
Understands main problems of the data mining area: clustering, classification, outlier analysis and associative patterns mining.
Familiar with mathematical foundations of each problem.
Is able to formally state data mining problem.
Able to choose methods to solve given problem.
Able to program the algorithms of most popular methods.
Able to interpret achieved results.
