Hybrid PCA-ILGC clustering approach for high dimensional data.
Aina MusdholifahSiti Zaiton Mohd HashimRazali NgahPublished in: SMC (2012)
Keyphrases
- high dimensional data
- dimensionality reduction
- low dimensional
- high dimensional
- principal component analysis
- dimension reduction
- linear discriminant analysis
- data points
- high dimensionality
- subspace clustering
- lower dimensional
- nearest neighbor
- variable weighting
- high dimensional data sets
- high dimensions
- input space
- data sets
- clustering high dimensional data
- similarity search
- original data
- manifold learning
- k means
- principal components analysis
- locally linear embedding
- low rank
- data analysis
- feature extraction
- high dimensional spaces
- small sample size
- high dimensional datasets
- dimensional data
- nonlinear dimensionality reduction
- sparse representation
- principal components
- subspace learning
- dimensionality reduction methods
- feature space
- multi dimensional
- high dimensional data analysis
- pattern recognition
- subspace methods
- clustering algorithm
- face recognition
- feature selection
- cluster analysis
- training set
- support vector machine