Can clustering scale sublinearly with its clusters? A variational EM acceleration of GMMs and k-means.
Dennis ForsterJörg LückePublished in: AISTATS (2018)
Keyphrases
- k means
- clustering algorithm
- cluster analysis
- unsupervised clustering
- data clustering
- fuzzy k means
- hierarchical clustering
- clustering quality
- cluster centers
- clustering approaches
- clustering result
- expectation maximization
- clustering method
- clustering framework
- agglomerative hierarchical clustering
- document clustering
- gaussian mixture model
- constrained clustering
- self organizing maps
- fuzzy c means
- classical clustering algorithms
- hierarchical agglomerative clustering
- hierarchical clustering algorithm
- model based clustering
- cluster structure
- spectral clustering
- affinity propagation
- semi supervised clustering algorithm
- agglomerative clustering
- bisecting k means
- cluster validity
- image segmentation
- fuzzy clustering
- incremental clustering
- validity indices
- partitional clustering
- graph clustering
- clustering analysis
- kohonen self organizing maps
- overlapping clusters
- fuzzy clustering algorithm
- clustering ensemble
- davies bouldin
- subspace clustering
- mixture model
- hidden markov random fields
- unsupervised learning
- rough k means
- validity measures
- scale space
- em algorithm
- kernel based clustering
- instance level constraints
- cluster ensemble
- initial cluster centers
- consensus clustering
- clustering solutions
- graph partitioning
- arbitrary shape