Semi-Supervised Kernel Clustering with Sample-to-Cluster Weights.
Stefan FaußerFriedhelm SchwenkerPublished in: PSL (2011)
Keyphrases
- semi supervised
- constrained clustering
- semi supervised clustering
- clustering algorithm
- clustering framework
- instance level constraints
- clustering approaches
- data clustering
- unsupervised clustering
- clustering method
- k means
- hierarchical clustering
- variable weighting
- semi supervised learning
- cluster analysis
- kernel based clustering
- overlapping clusters
- unsupervised learning
- pairwise constraints
- hidden markov random fields
- semi supervised clustering algorithm
- data points
- cluster centers
- hierarchical clustering algorithm
- kernel function
- cluster membership
- active learning
- cluster centroids
- normalized cut
- kernel methods
- support vector
- linear combination
- metric learning
- clustering procedure
- inter cluster
- supervised learning
- labeled data
- pair wise constraints
- kernel learning
- pairwise
- intra cluster
- cluster structure
- validity measures
- clustering scheme
- clustering quality
- density based clustering algorithm
- unlabeled data
- cluster ensemble
- similarity function
- disjoint clusters
- subspace clustering
- similarity matrix
- cluster validation
- multiple kernel learning
- kernel matrix
- clustering result
- semi supervised classification
- high dimensional data
- self organizing maps
- labeled samples
- document clustering
- unlabeled samples
- spectral clustering
- fuzzy c means
- low rank
- clustering analysis