Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 65 tok/s
Gemini 2.5 Pro 47 tok/s Pro
GPT-5 Medium 39 tok/s Pro
GPT-5 High 32 tok/s Pro
GPT-4o 97 tok/s Pro
Kimi K2 164 tok/s Pro
GPT OSS 120B 466 tok/s Pro
Claude Sonnet 4 38 tok/s Pro
2000 character limit reached

Speeding Up Constrained $k$-Means Through 2-Means (1808.04062v1)

Published 13 Aug 2018 in cs.CG, cs.DM, and cs.DS

Abstract: For the constrained 2-means problem, we present a $O\left(dn+d({1\over\epsilon}){O({1\over \epsilon})}\log n\right)$ time algorithm. It generates a collection $U$ of approximate center pairs $(c_1, c_2)$ such that one of pairs in $U$ can induce a $(1+\epsilon)$-approximation for the problem. The existing approximation scheme for the constrained 2-means problem takes $O(({1\over\epsilon}){O({1\over \epsilon})}dn)$ time, and the existing approximation scheme for the constrained $k$-means problem takes $O(({k\over\epsilon}){O({k\over \epsilon})}dn)$ time. Using the method developed in this paper, we point out that every existing approximating scheme for the constrained $k$-means so far with time $C(k, n, d, \epsilon)$ can be transformed to a new approximation scheme with time complexity ${C(k, n, d, \epsilon)/ k{\Omega({1\over\epsilon})}}$.

Citations (1)
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-Up Questions

We haven't generated follow-up questions for this paper yet.

Authors (2)