Title: Angle K-Means
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Clustering, K-Means, Accelerate, Angle

Abstract: We propose an accelerated exact $k$-means algorithm, Angle $k$-means.
As its name suggests, the algorithm mainly leverages angular relationships between data points and cluster centers 
to reduce computational overhead. Although grounded in straightforward geometric principles, 
it delivers substantial performance improvements in empirical evaluations.
In contrast to existing acceleration techniques, our model introduces no new hyperparameters,
preserving full compatibility with standard $k$-means.
Theoretical analysis shows that Angle $k$-means maintains linear time complexity 
with respect to both sample size and dimensionality, 
while empirical evaluations on diverse real-world datasets demonstrate 
significant speedup over state-of-the-art algorithms such as ball $k$-means and Exp-ns.

TL;DR: Fast K-Means via Enhanced Triangle Inequality