Lloyd's algorithm
PulseAugur coverage of Lloyd's algorithm — every cluster mentioning Lloyd's algorithm across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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New Gromov-Wasserstein quantization method extends k-means clustering
A new paper introduces Gromov-Wasserstein (GW) quantization as an extension of traditional k-means clustering. This method not only clusters data points but also considers the ambient geometry of the space, offering new…
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Transformer architecture precisely implements k-means clustering algorithm
A new research paper details the creation of a "k-means transformer" architecture that can precisely execute Lloyd's algorithm for k-means clustering. The study theoretically proves and empirically demonstrates that sta…
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Auto-CoT generates diverse LLM reasoning demos by clustering questions
Auto-CoT is a method for automatically generating few-shot chain-of-thought demonstrations for large language models. Instead of manually crafting examples, Auto-CoT clusters questions from a pool and selects one repres…
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New one-bit clustering method for sub-Gaussian mixture models proposed
Researchers have introduced a novel one-bit clustering method designed for two-component sub-Gaussian mixture models. This technique utilizes a single bit per sample entry, processed through a dithered quantizer. The me…
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Lloyd's algorithm clustering consistency proven under perturbed samples
Researchers have analyzed the consistency of Lloyd's algorithm, a popular unsupervised clustering method, when applied to perturbed data. They demonstrated that even with small perturbations, the algorithm maintains an …
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New Hartigan k-means variant improves clustering results by up to 10%
Researchers have developed an improved version of the Hartigan k-means clustering algorithm, building upon its known advantages over Lloyd's algorithm. This minor variation reportedly yields an additional 2-5% improveme…