The seeding algorithm for spherical \(k\)-means clustering with penalties

DOI10.1007/s10878-020-00569-1zbMath1502.90150OpenAlexW3017047060MaRDI QIDQ2082212

Longkun Guo, Dongmei Zhang, Min Li, Sai Ji, Da-Chuan Xu

Publication date: 4 October 2022

Published in: Journal of Combinatorial Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10878-020-00569-1

zbMATH Keywords

approximation algorithmpenaltyspherical \(k\)-means clustering

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Combinatorial optimization (90C27)

Related Items (4)

An improved primal-dual approximation algorithm for the k-means problem with penalties ⋮ Approximation Algorithms for Matroid and Knapsack Means Problems ⋮ Approximation Algorithms for Spherical k-Means Problem with Penalties Using Local Search Techniques ⋮ Continuous regularized least squares polynomial approximation on the sphere

Uses Software

• k-means++

Cites Work

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• \(k\)-means requires exponentially many iterations even in the plane
• A local search approximation algorithm for \(k\)-means clustering
• Clustering large graphs via the singular value decomposition
• NP-hardness of Euclidean sum-of-squares clustering
• The seeding algorithm for \(k\)-means problem with penalties
• The seeding algorithms for spherical \(k\)-means clustering
• Local Search Yields Approximation Schemes for $k$-Means and $k$-Median in Euclidean and Minor-Free Metrics
• Least squares quantization in PCM
• Better Guarantees for $k$-Means and Euclidean $k$-Median by Primal-Dual Algorithms
• Spherical k-Means++ Clustering
• Concept decompositions for large sparse text data using clustering

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