Course : Cluster Analysis & Unsupervised ML in Python
Video Mins completed : 50 mins
Last Video # completed : 22
Notes
K-Means Clustering
Cost function : Coordinate distance
Soft K-means : Assigns probability of a point belonging to a certain cluster based on the distance from the cluster mean.
Better than Hard K-means which assigns a 100% probability to one class.
K-Means clustering fails for data clusters shaped as
- donut
- elongated clusters
- different density clusters.
Can only look for spherical clusters
Disadvantages
- Need to choose K
- Local Minima tripping the clustering
- Sensitive to initial configuration
- Doesn’t take into account the density of the cluster