k-means clustering
PulseAugur coverage of k-means clustering — every cluster mentioning k-means clustering across labs, papers, and developer communities, ranked by signal.
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New Shapley-inspired k-means algorithm enhances feature weighting
Researchers have developed SHARK (Shapley Reweighted k-means), a novel feature-weighting method for clustering algorithms that avoids the need for additional hyperparameter tuning. This approach leverages Shapley values…
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Generative Spoken Language Models Achieve Quality Speech at Lower Bitrates
A new research paper published on arXiv explores the effectiveness of Generative Spoken Language Modeling (GSLM) for speech synthesis and continuation. The study investigates how varying segmentation widths and cluster …
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New method pre-trains Tsetlin Machines with language model clusters for interpretability
Researchers have developed a novel framework to enhance the interpretability of Tsetlin Machines (TMs) by integrating knowledge from pre-trained language models like BERT. This method groups text samples into semantic c…
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AI framework learns fracture phenotypes without human labels
Researchers have developed a novel label-agnostic framework for characterizing tibial plateau fractures using self-supervised learning. This approach bypasses the need for human-assigned labels, which are prone to inter…
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BioArtlas computational tool maps complex bioart field
Joonhyung Bae has developed BioArtlas, a computational tool designed to organize and analyze the field of bioart. This atlas represents bioartworks across multiple curated dimensions, enabling comparison based on concep…
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New Method Uses Optimal Transport for Geometric Domain Adaptation
Researchers have developed a novel method for domain adaptation in linear regression using optimal transport. This approach leverages theoretical insights to recover geometric transformations like rotations and translat…
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Speech models compressed using parameter clustering
Researchers have developed a new method for compressing speech foundation models without requiring additional data or retraining. This approach utilizes channelwise clustering with k-means to achieve parameter compressi…
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New ensemble deep clustering method improves EHR patient stratification
Researchers have developed an ensemble-based deep clustering approach to improve patient stratification using electronic health records (EHRs). This new method aggregates cluster assignments from multiple embedding dime…
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Graph clustering outperforms K-means for speech term discovery
Researchers have published a paper proposing graph-based clustering as a superior method for unsupervised term discovery in speech processing. Unlike traditional center-based methods like K-means, which create uniform d…
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Flash-GMM kernel speeds up GMM clustering 20x, enables larger datasets
Researchers have developed Flash-GMM, a new fused Triton kernel designed for efficient Gaussian Mixture Model (GMM) computations on GPUs. This kernel significantly reduces memory requirements by avoiding the materializa…
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Airline profit cycles analyzed with PCA, revealing fewer clusters
A new paper explores the dimensionality and orthogonality of airline profit cycles using Principal Component Analysis (PCA) and Kernel PCA. The research replicates a previous clustering experiment, finding that a six-cl…
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UniFair framework enhances clustering fairness with dual optimization
Researchers have introduced UniFair, a novel framework designed to enhance fairness in clustering algorithms. This approach simultaneously optimizes for separation fairness, ensuring protected groups are distant from de…
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Satellite data tracks urban air pollution with clustering
Researchers have developed a satellite-based method to track urban nitrogen dioxide pollution using data from the Sentinel-5P satellite. This framework focuses on distributional metrics like median and upper-tail percen…
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New CDL index improves unsupervised clustering validation
Researchers have introduced a new clustering validation index called Central Description Length (CDL). This index aims to improve the selection of clustering algorithms and hyperparameters in unsupervised machine learni…
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New PE-means algorithm improves private k-means clustering by 20%
Researchers have developed PE-means, a new algorithm for differentially private k-means clustering. This method improves upon existing techniques by using a private histogram with constant sensitivity, rather than direc…
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New CLUBench benchmark evaluates AI clustering algorithms
A new benchmark called CLUBench has been developed to evaluate clustering algorithms across various data types, including tabular, text, and image data. The benchmark comprises 24 algorithms and 131 datasets, involving …
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New retrieval method replaces K-means with sparse coding for faster, more accurate results
Researchers have introduced Single-stage Sparse Retrieval (SSR), a new method for efficient multi-vector retrieval that bypasses traditional K-means clustering. SSR utilizes Sparse Autoencoders to create high-dimensiona…
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New AI Clustering Method Uses Stochastic Dominance for Risk-Based Asset Allocation
Researchers have developed a novel clustering framework that leverages Stochastic Dominance (SD) theory and machine learning to better group assets based on risk preferences. This approach moves beyond traditional geome…
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New Firefly Algorithm variant enhances automatic data clustering
Researchers have developed a new variant of the Firefly Algorithm designed to improve automatic data clustering. This enhanced algorithm addresses limitations in traditional methods like K-Means, particularly their diff…
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Machine learning framework aids diabetes detection and subtype analysis
Researchers have developed a novel three-stage machine learning framework to address the complexities of diabetes management. The first stage benchmarks various classifiers for detecting diabetes and identifies key pred…