Detecting Meaningful Clusters From High-Dimensional Data: A Strongly Consistent Sparse Center-Based Clustering Approach.
Saptarshi ChakrabortySwagatam DasPublished in: IEEE Trans. Pattern Anal. Mach. Intell. (2022)
Keyphrases
- high dimensional data
- meaningful clusters
- high dimensional
- data points
- subspace clustering
- sparse representation
- subspace clusters
- dimensionality reduction
- low dimensional
- high dimensionality
- nearest neighbor
- center based clustering
- dimension reduction
- data analysis
- data sets
- similarity search
- microarray data analysis
- input space
- clustering high dimensional data
- manifold learning
- original data
- microarray data
- lower dimensional
- nonlinear dimensionality reduction
- clustering algorithm
- feature space
- pattern recognition
- low rank
- gene expression data
- random projections
- neural network
- high dimensional datasets
- locally linear embedding
- high dimensional spaces
- dimensional data
- underlying manifold
- knowledge discovery
- euclidean distance
- database
- input data
- high dimensional data sets
- training data
- euclidean space
- multi dimensional