Using Projection-Based Clustering to Find Distance- and Density-Based Clusters in High-Dimensional Data.
Michael C. ThrunAlfred UltschPublished in: J. Classif. (2021)
Keyphrases
- subspace clustering
- high dimensional data
- clustering algorithm
- subspace clusters
- data points
- subspace clustering algorithms
- clustering method
- high dimensionality
- nearest neighbor
- clustering high dimensional data
- dimensionality reduction
- high dimensional datasets
- high dimensional
- low dimensional
- cluster structure
- high dimensions
- data sets
- similarity search
- data analysis
- input space
- intrinsic dimension
- dense regions
- euclidean distance
- high dimensional data sets
- data distribution
- density based clustering
- dimension reduction
- manifold learning
- high dimensional feature spaces
- distance function
- inter cluster
- arbitrary shape
- gene expression data
- high dimensional spaces
- spatial clustering
- intra cluster
- nonlinear dimensionality reduction
- input data
- original data
- data clustering
- distance metric
- outlier detection
- text data
- hierarchical clustering
- similarity measure
- spectral clustering
- document clustering
- machine learning
- variable weighting
- database