Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data.
Levent ErtözMichael S. SteinbachVipin KumarPublished in: SDM (2003)
Keyphrases
- high dimensional data
- subspace clustering
- data points
- high dimensional datasets
- input space
- nearest neighbor
- data distribution
- high dimensional
- dimensionality reduction
- low dimensional
- data sets
- data analysis
- cluster structure
- high dimensionality
- dimension reduction
- high dimensions
- subspace clusters
- original data
- manifold learning
- clustering algorithm
- similarity search
- high dimensional feature spaces
- arbitrary shape
- input data
- linear discriminant analysis
- lower dimensional
- high dimensional spaces
- clustering high dimensional data
- database
- low dimensional manifolds
- feature selection
- high dimensional data sets
- dimensional data
- text data
- probability density
- underlying manifold