To introduce students to specific knowledge and skills in data grouping using appropriate software tools. Specific knowledge and skills are developed through project assignments.
1. Introduction and motivation for data clustering. Problem definition. Various applied case studies.
2. Preparing data for clustering. Overview of clustering algorithms.
3. Data clustering: Representative data set. Application of appropriate algorithm. Selection of partition with most appropriate number of clusters – indices.
4. Analysis and evaluation of the obtained results.
After completing the course, the student will be able to:
1. Identify problems from appropriate data sets to which the acquired knowledge of data grouping can be applied.
2. Choose an appropriate method for grouping entities into clusters that have the smallest mutual distance within the selected data set.
3. Evaluate and interpret the obtained results of data grouping using clustering algorithms.
