Inapproximability of Maximum Diameter Clustering for Few Clusters.
Henry FleischmannKyrylo KarlovKarthik C. S.Ashwin PadakiStepan ZharkovPublished in: CoRR (2023)
Keyphrases
- clustering algorithm
- hierarchical clustering
- overlapping clusters
- cluster analysis
- clustering method
- data clustering
- clustering scheme
- graph clustering
- k means
- clustering framework
- fuzzy clustering
- document clustering
- self organizing maps
- validity measures
- data points
- clustering result
- agglomerative hierarchical clustering
- fuzzy k means
- density based clustering algorithm
- cluster centers
- unsupervised clustering
- clustering quality
- incremental clustering
- cluster validation
- disjoint clusters
- intra cluster
- fuzzy clustering algorithm
- classical clustering algorithms
- clustering approaches
- density based clustering
- inter cluster
- arbitrary shape
- clustering procedure
- agglomerative clustering
- spatial clustering
- spectral clustering
- subspace clustering
- hierarchical clustering algorithm
- cluster structure
- constrained clustering
- clustering analysis
- categorical data
- approximation algorithms
- kohonen self organizing maps
- outlier detection
- fuzzy c means
- cluster membership
- cluster validity
- hierarchical agglomerative clustering
- maximum distance
- synthetic datasets
- parameter free
- bayesian information criterion
- similarity matrix
- dissimilarity matrix
- data objects
- affinity propagation
- search results clustering
- unsupervised learning