Descent of Lloyd’s algorithm
Proposition taught in K-means clustering, part of Statistical learning
Descent of Lloyd’s algorithm
| Type | Proposition |
| Taught in | K-means clustering |
| Part | Statistical learning |
Each step of the algorithm does not increase \widetilde W. With the tie convention above and nonempty clusters throughout, the algorithm stops after finitely many assignment changes at a Lloyd fixed point. This is an optimum with respect to each of the two alternating steps separately. It need not be a global optimum.
Used in
- Taught in K-means clustering, where the derivation and examples are.
- All 8 questions on K-means clustering
See also
Lloyd’s K-means algorithm · Why the raw sum of squares cannot choose · K-means criterion · Within-cluster scatter · From pairs to cluster means · Why the partitions are not all searched · Initialising k-means · Choosing K: the gap statistic