Using projections to visually cluster high-dimensional data.
Alexander HinneburgDaniel A. KeimMarkus WawryniukPublished in: Comput. Sci. Eng. (2003)
Keyphrases
- high dimensional data
- subspace clustering
- data points
- high dimensional
- nearest neighbor
- dimensionality reduction
- data sets
- variable weighting
- low dimensional
- cluster structure
- high dimensionality
- data analysis
- high dimensions
- data distribution
- clustering high dimensional data
- similarity search
- subspace clusters
- dimension reduction
- dimensional data
- input space
- sparse representation
- clustering algorithm
- high dimensional spaces
- original data
- lower dimensional
- high dimensional data sets
- linear discriminant analysis
- high dimensional datasets
- data clustering
- low rank
- manifold learning
- gene expression data
- euclidean distance
- neural network
- cluster analysis
- pattern recognition
- k means
- database
- small sample size
- subspace learning
- machine learning
- feature space