Papers › The k-means-u* algorithm: non-local jumps and greedy retries improve k-means++ clustering
The k-means-u* algorithm: non-local jumps and greedy retries improve k-means++ clustering
Bernd Fritzke
We present a new clustering algorithm called k-means-u* which in many cases is able to significantly improve the clusterings found by k-means++, the current de-facto standard for clustering in Euclidean spaces. First we introduce the k-means-u algorithm which starts from a result of k-means++ and attempts to improve it with a sequence of non-local "jumps" alternated by runs of standard k-means. Each jump transfers the "least useful" center towards the center with the largest local error, offset by a small random vector. This is continued as long as the error decreases and often leads to an improved solution. Occasionally k-means-u terminates despite obvious remaining optimization possibilities. By allowing a limited number of retries for the last jump it is frequently possible to reach better local minima. The resulting algorithm is called k-means-u* and dominates k-means++ wrt. solution quality which is demonstrated empirically using various data sets. By construction the logarithmic quality bound established for k-means++ holds for k-means-u* as well.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections