Clustering methods help to design segment-based strategies in business, e.g., design marketing campaigns, improve the classification of customer preferences, or to assess credit risk more accurate just to name a few examples. These methods are also useful to reveal subgroups, or in a larger population that share similar characteristics.
This course introduces different clustering techniques to find smaller groups within a larger population as well as methods to analyse and understand which characteristics distinguish each cluster. Further, the course introduces the idea of dynamic clustering, or time dependent clustering, where we learn how to follow movements between clusters through time.
The focus of the course is practical and analytical. preparing the students for jobs as data analysts/scientists or for a MSc in Data Science for Business or in Business Analytics.
- Unsupervised vs Supervised learning
- Key clustering algorithms, including: Hierarchical clustering, K-means, and density-based clustering methods
- Cluster analysis on different feature spaces
- Statistical methods to analyze the clusters
- Clustering vs Dynamic clustering
This is an excerpt from the complete course description for the course. If you are an active student at BI, you can find the complete course descriptions with information on eg. learning goals, learning process, curriculum and exam at portal.bi.no. We reserve the right to make changes to this description.