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K-means++ algorithm modified for improved approximation ratio

By PulseAugur Editorial · [1 sources] ·

Researchers have proposed a modification to the k-means++ algorithm, a common method for initializing k-means clustering. The standard algorithm has a worst-case expected approximation ratio of \Theta(\log k) for a fixed number of centers k. However, when the number of centers k is chosen uniformly from a range \{K, \ldots, 2K-1\}, the modified k-means++ algorithm achieves an O(1)-approximation with constant probability. AI

IMPACT This research could lead to more efficient and accurate clustering in machine learning applications.

RANK_REASON The cluster contains an academic paper detailing a new algorithmic approach. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

K-means++ algorithm modified for improved approximation ratio

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The cluster contains an academic paper detailing a new algorithmic approach. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Vaclav Rozhon ·

    Randomizing the Number of Centers in k-means++

    arXiv:2607.26202v1 Announce Type: cross Abstract: The $k$-means++ algorithm is a standard and widely used seeding method for $k$-means clustering, but for a fixed number $k$ of centers its worst-case expected approximation ratio is $\Theta(\log k)$. We consider the same algorithm…

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