arXiv Machine Learning July 20, 2026 By Ravil Mussabayev, Rustam Mussabayev, Zukhra Yerdaliyeva, Kuldeyev Nursultan

Data-Native Global Optimization for Big Data K-means Clustering

Read the original on arXiv Machine Learning →

arXiv:2607. 15835v1 Announce Type: new Abstract: Big data clustering remains challenging: the Minimum Sum-of-Squares Clustering (MSSC) problem underlying K-means is NP-hard, and existing methods either reach poor local minima or require prohibitive metaheuristic hybrids.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

agents

arXiv Machine Learning

Aug 5

Strong bounds for large-scale Minimum Sum-of-Squares Clustering

arXiv:2502. 08397v3 Announce Type: replace-cross Abstract: Clustering is a fundamental technique in data analysis and machine learning, used to group similar data points together.

By Anna Livia Croella, Veronica Piccialli, Antonio M. Sudoso

arXiv Machine Learning

Jul 23

Efficient Clustering with Provable Guardrails for LLM Inference at Scale

arXiv:2607. 19704v1 Announce Type: new Abstract: Scaling LLM-based applications to millions of users is bottlenecked by the inference cost and latency of modern foundation models.

By Longshaokan Wang, Wai Tsang Keung, Punit Ghodasara, Roman Wang, Ali Dashti, Francesc Moreno-Noguer

llms rag benchmarks safety

arXiv Machine Learning

Jun 16

SILAGE: Memory-Efficient, Full-Gradient-Free Nonconvex Optimization for Nested Finite Sums

arXiv:2606. 15832v1 Announce Type: new Abstract: Empirical risk minimization on massive datasets naturally exhibits a nested double finite-sum structure, where $N=nm$ total samples are logically or physically partitioned into $n$ blocks of size $m$ (e.

By Igor Sokolov, Laurent Condat, Peter Richt\'arik

efficiency benchmarks

arXiv Machine Learning

Jul 28

The K-SCAN Clustering Algorithm

arXiv:2607. 24537v1 Announce Type: new Abstract: In the Big Data era, the scalability of clustering algorithms constitutes a key challenge.

By Filip Kosiorowski, Grzegorz Sroka

efficiency

arXiv Machine Learning

Jun 18

FOSC-X: An Extended Framework for Optimal Local Cuts and Non-Horizontal Cluster Selection from Clustering Hierarchies

arXiv:2606. 18972v1 Announce Type: cross Abstract: Extracting a flat clustering solution from a hierarchy is a common task in practical cluster analysis and can be formulated as an optimisation problem.

By Connor Simpson, Ricardo J. G. B. Campello

efficiency

arXiv Machine Learning

Jul 7

Sensitivity Sampling with Predictions for k-Means Clustering

arXiv:2607. 04949v1 Announce Type: new Abstract: We study the problem of k-means clustering on large datasets.

By Cristian Boldrin, Fabio Vandin

benchmarks

Related stories

Strong bounds for large-scale Minimum Sum-of-Squares Clustering

Efficient Clustering with Provable Guardrails for LLM Inference at Scale

SILAGE: Memory-Efficient, Full-Gradient-Free Nonconvex Optimization for Nested Finite Sums

The K-SCAN Clustering Algorithm

FOSC-X: An Extended Framework for Optimal Local Cuts and Non-Horizontal Cluster Selection from Clustering Hierarchies

Sensitivity Sampling with Predictions for k-Means Clustering