An effective variant of the Hartigan $k$-means algorithm
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908989747363840 |
|---|---|
| author | Clément, François Steinerberger, Stefan |
| author_facet | Clément, François Steinerberger, Stefan |
| contents | The k-means problem is perhaps the classical clustering problem and often synonymous with Lloyd's algorithm (1957). It has become clear that Hartigan's algorithm (1975) gives better results in almost all cases, Telgarsky-Vattani note a typical improvement of $5\%$ -- $10\%$. We point out that a very minor variation of Hartigan's method leads to another $2\%$ -- $5\%$ improvement; the improvement tends to become larger when either dimension or $k$ increase. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_21798 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An effective variant of the Hartigan $k$-means algorithm Clément, François Steinerberger, Stefan Machine Learning The k-means problem is perhaps the classical clustering problem and often synonymous with Lloyd's algorithm (1957). It has become clear that Hartigan's algorithm (1975) gives better results in almost all cases, Telgarsky-Vattani note a typical improvement of $5\%$ -- $10\%$. We point out that a very minor variation of Hartigan's method leads to another $2\%$ -- $5\%$ improvement; the improvement tends to become larger when either dimension or $k$ increase. |
| title | An effective variant of the Hartigan $k$-means algorithm |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.21798 |