Approximation Algorithms for Clustering to Minimize the Sum of Diameters
Explore this paper's citation graph
Summary
It is shown that, unless P = NP, for any rho ge 1, there is no polynomial time approximation algorithm that can provide a performance guarantee of rho even when the number of clusters is fixed at 3.
- Type
- article
- Published
- 2000-02-01
- Cited by
- 56
- References
- 33
- OpenAlex
- https://openalex.org/W1850573590
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:16969419
Keywords
Approximation algorithm, Triangle inequality, Mathematics, Combinatorics, Cluster analysis
References
- Book review: Approximation Algorithms for NP-hard Problems. Edited by Dorit S. Hochbaum (PWS, 1997)
- Some Geometric Clustering Problems
- A sublinear time approximation scheme for clustering in metric spaces
- Algorithms for Clustering Data
- Clustering to Minimize the Maximum Intercluster Distance
- Approximation Algorithms for NP-Hard Problems
- On Approximate Geometric k -Clustering
- Minimum sum of diameters clustering
- Cluster analysis and mathematical programming
- Computers and Intractability: A Guide to the Theory of NP-Completeness
- Optimal Packing and Covering in the Plane are NP-Complete
- Incremental clustering and dynamic information retrieval
- Approximation algorithms for projective clustering
- Approximation Algorithms for Min-sum p-clustering
- Bicriteria Network Design Problems
- Information retrieval algorithms: a survey
- A unified approach to approximation algorithms for bottleneck problems
- An Approximation Algorithm for Clustering Graphs with Dominating Diametral Path
- Near-Linear Time Construction of Sparse Neighborhood Covers
- The Budgeted Maximum Coverage Problem
Cited by
- Spectral min-max cut for graph partitioning and data clustering
- Clusters and covers: geometric set cover algorithms
- A Framework for Promotion Analysis in Multi-Dimensional Space
- Consensus Algorithms for Trees and Strings
- Joint cluster analysis of attribute data and relationship data
- On clustering to minimize the sum of radii
- A generalized minimum cost k-clustering
- On Minimum Sum of Radii and Diameters Clustering
- Multi Cover of a Polygon Minimizing the Sum of Areas
- Clustering to minimize the sum of cluster diameters
- Online Clustering with Variable Sized Clusters
- Efficient Table Anonymization for Aggregate Query Answering
- Online algorithms for clustering problems
- CLUSTERING WITH CLUSTER-LEVEL CONSTRAINTS
- A min-max cut algorithm for graph partitioning and data clustering
- Approximate Closest Community Search in Networks
- Efficient on-line algorithm for maintaining k-cover of sparse bit-strings
- Approximation Algorithms for Clustering Problems with Lower Bounds and Outliers
- Approximation algorithms for clustering problems
- Approximation Algorithms for Clustering and Facility Location Problems
Related papers
- Clustering to minimize the sum of cluster diameters
- Polynomial time approximation schemes for base station coverage with minimum total radii
- On clustering to minimize the sum of radii
- Minimum-cost coverage of point sets by disks
- A Best Possible Heuristic for the k-Center Problem
- Minimum sum of diameters clustering
- Online facility location