It focuses on spatial clustering of point data, based on classic k-means and k-medoids. More advanced examples focuses on heuristic aspects of clustering, showing the importance of decisions made by users, including but not limited to:
- setting initial seeds / initial solution
- number of seeds ( = number of clusters)
- cost function
- stopping criteria and temporaty growth blocking criteria
Data used as examples comes from https://mapplab.pl
This workshop is created with help of @Zxsmors