On Discovery of Extremely Low-Dimensional Clusters using Semi-Supervised Projected Clustering.
Kevin Y. YipDavid W. CheungMichael K. NgPublished in: ICDE (2005)
Keyphrases
- low dimensional
- semi supervised
- data points
- clustering algorithm
- constrained clustering
- clustering framework
- semi supervised clustering algorithm
- manifold learning
- cluster analysis
- hierarchical clustering
- clustering approaches
- instance level constraints
- high dimensional data
- overlapping clusters
- unsupervised clustering
- semi supervised clustering
- high dimensional
- clustering method
- data clustering
- unlabeled data
- k means
- input space
- self organizing maps
- labeled data
- subspace learning
- unsupervised learning
- incremental clustering
- intra cluster
- graph clustering
- clustering quality
- subspace clustering
- high dimensional data space
- dimensionality reduction
- semi supervised learning
- hierarchical clustering algorithm
- spectral clustering
- fuzzy clustering
- inter cluster
- density based clustering algorithm
- document clustering
- clustering scheme
- clustering result
- cluster centers
- density based clustering
- multidimensional scaling
- pairwise constraints
- arbitrary shape
- cluster validation
- validity measures
- cluster structure
- disjoint clusters
- agglomerative hierarchical clustering
- model based clustering
- hierarchical agglomerative clustering
- pair wise constraints
- supervised learning
- pairwise
- similarity matrix
- fuzzy c means
- input data
- principal component analysis
- graph embedding
- cluster ensemble
- similar objects
- meaningful clusters
- data objects
- fuzzy k means
- dimension reduction
- affinity measure
- nearest neighbor
- data mining