Coresets for Fuzzy K-Means with Applications

DOI10.4230/LIPIcs.ISAAC.2018.46OpenAlexW2905510785MaRDI QIDQ5091038

Kathrin Bujna, Sascha Brauer, Johannes Blömer

Publication date: 21 July 2022

Full work available at URL: https://drops.dagstuhl.de/opus/volltexte/2018/9994/pdf/LIPIcs-ISAAC-2018-46.pdf/

zbMATH Keywords

clusteringapproximation algorithmsstreamingcoresetsfuzzy \(K\)-means

Mathematics Subject Classification ID

Algorithms in computer science (68Wxx)

Cites Work

• Unnamed Item
• Unnamed Item
• Smaller coresets for \(k\)-median and \(k\)-means clustering
• Local convergence of the fuzzy c-means algorithms
• Optimality tests for fixed points of the fuzzy c-means algorithm
• Extensions of Lipschitz mappings into a Hilbert space
• On Coresets for k-Median and k-Means Clustering in Metric and Euclidean Spaces and Their Applications
• On coresets for k-means and k-median clustering
• A PTAS for k-means clustering based on weak coresets
• Convergence theory for fuzzy c-means: Counterexamples and repairs
• Decomposable searching problems I. Static-to-dynamic transformation
• Least squares quantization in PCM
• A unified framework for approximating and clustering data
• Turning Big data into tiny data: Constant-size coresets for k-means, PCA and projective clustering
• A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters

This page was built for publication: Coresets for Fuzzy K-Means with Applications