High-dimensional Data Clustering Using K-means Subspace Feature Selection.
Xiao-Dong WangRung-Ching ChenFei YanPublished in: J. Netw. Intell. (2019)
Keyphrases
- high dimensional data
- k means
- high dimensionality
- dimensionality reduction
- feature selection
- subspace clustering
- low dimensional
- variable weighting
- dimension reduction
- clustering method
- data points
- clustering algorithm
- high dimensional
- nearest neighbor
- clustering high dimensional data
- data sets
- data analysis
- data clustering
- original data
- similarity search
- high dimensional data sets
- gene expression data
- lower dimensional
- manifold learning
- subspace learning
- cluster analysis
- feature space
- low rank
- high dimensional datasets
- small sample size
- cluster structure
- high dimensional spaces
- subspace clusters
- sparse representation
- input space
- locally linear embedding
- feature extraction
- principal component analysis
- text data
- input data
- unsupervised learning
- hierarchical clustering
- dimensional data
- text clustering
- subspace clustering algorithms
- text categorization
- principal components analysis
- fuzzy c means
- clustering quality
- cluster centers
- informative features
- pattern recognition
- support vector machine
- linear discriminant analysis