Approximation algorithms for fuzzy \(C\)-means problem based on seeding method

DOI10.1016/j.tcs.2021.06.035OpenAlexW3173970056MaRDI QIDQ1981782

Jian-Xin Liu, Min Li, Qian Liu, Yang Zhou

Publication date: 6 September 2021

Published in: Theoretical Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.tcs.2021.06.035

zbMATH Keywords

approximation algorithmapproximation ratiofuzzy \(C\)-means problemseeding algorithm

Mathematics Subject Classification ID

Theory of computing (68Qxx)

Uses Software

• k-means++

Cites Work

• Unnamed Item
• Clustering large graphs via the singular value decomposition
• The seeding algorithm for \(k\)-means problem with penalties
• The seeding algorithms for spherical \(k\)-means clustering
• Approximation algorithms for metric facility location and k -Median problems using the primal-dual schema and Lagrangian relaxation
• Least squares quantization in PCM
• 10.1162/153244303321897735
• Better Guarantees for $k$-Means and Euclidean $k$-Median by Primal-Dual Algorithms
• A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters

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