Clustering in advanced data analytics. Bottom-up? Top-down? Or both?
Cluster analysis, or simply clustering, is the task of assigning a set of observations (samples, objects, cases, items, …) into groups (called clusters) in such a way that the observations in the same cluster are more similar in some sense to each other than to those in other clusters. In addition to the core data analytics engines (PCA, PLS, OPLS and O2PLS), SIMCA 14.1 offers tools for both top-down and bottom-up clustering. Attend this webinar and get familiar with the basics of cluster analysis, and learn how outputs from such modeling can facilitate further data mining activities.
Topics for this webinar
- An introduction to SIMCA 14 and its cluster analysis capabilities
- Cluster analysis – what it is and when to use it and the values it creates
- Top-down clustering or bottom-up clustering?
- Hierarchical cluster analysis, HCA, for observations and variables
- The PLS-Tree
- In-depth data mining using combinations of clustering and projection methods