Finding non-redundant, statistically significant regions in high dimensional data: a novel approach to projected and subspace clustering.
Gabriela MoiseJörg SanderPublished in: KDD (2008)
Keyphrases
- statistically significant
- subspace clustering
- high dimensional data
- clustering high dimensional data
- dimensionality reduction
- dense regions
- high dimensionality
- high dimensional
- learning styles
- data sets
- control group
- nearest neighbor
- low dimensional
- data analysis
- subspace clusters
- similarity search
- data points
- statistical significance
- input space
- pearson correlation
- dimension reduction
- complex data
- clustering algorithm
- clustering method
- text data
- high dimensional datasets
- lower dimensional
- manifold learning
- high dimensional feature spaces
- neural network
- original data
- linear discriminant analysis
- multi dimensional
- high dimensional spaces
- machine learning
- subspace clustering algorithms