Subspace search and visualization to make sense of alternative clusterings in high-dimensional data.
Andrada TatuFabian MaassInes FärberEnrico BertiniTobias SchreckThomas SeidlDaniel A. KeimPublished in: IEEE VAST (2012)
Keyphrases
- high dimensional data
- data analysis
- low dimensional
- subspace clustering
- dimensionality reduction
- high dimensionality
- high dimensional
- nearest neighbor
- clustering high dimensional data
- high dimensions
- data points
- lower dimensional
- similarity search
- input space
- data sets
- original data
- low rank
- alternative clusterings
- high dimensional datasets
- dimension reduction
- subspace learning
- linear discriminant analysis
- manifold learning
- sparse representation
- high dimensional spaces
- nearest neighbor search
- feature space
- dimensional data
- principal component analysis
- input data
- pattern recognition
- nonlinear dimensionality reduction
- clustering algorithm
- text data
- multi dimensional
- small sample size
- high dimensional data sets
- feature selection