all AI news
A Faster $k$-means++ Algorithm
Feb. 15, 2024, 5:43 a.m. | Jiehao Liang, Somdeb Sarkhel, Zhao Song, Chenbo Yin, Junze Yin, Danyang Zhuo
cs.LG updates on arXiv.org arxiv.org
Abstract: $k$-means++ is an important algorithm for choosing initial cluster centers for the $k$-means clustering algorithm. In this work, we present a new algorithm that can solve the $k$-means++ problem with nearly optimal running time. Given $n$ data points in $\mathbb{R}^d$, the current state-of-the-art algorithm runs in $\widetilde{O}(k )$ iterations, and each iteration takes $\widetilde{O}(nd k)$ time. The overall running time is thus $\widetilde{O}(n d k^2)$. We propose a new algorithm \textsc{FastKmeans++} that only takes in …
abstract algorithm art arxiv cluster clustering clustering algorithm cs.ds cs.lg current data faster running solve state type work
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Business Intelligence Architect - Specialist
@ Eastman | Hyderabad, IN, 500 008