Geodesic distance based fuzzy c-medoid clustering - searching for central points in graphs and high dimensional data.
András KirályÁgnes Vathy-FogarassyJános AbonyiPublished in: Fuzzy Sets Syst. (2016)
Keyphrases
- high dimensional data
- data points
- high dimensionality
- dimensionality reduction
- subspace clustering
- low dimensional
- nearest neighbor
- dimension reduction
- euclidean distance
- high dimensions
- high dimensional
- data sets
- high dimensional datasets
- high dimensional data sets
- outlier detection
- geodesic distance
- similarity search
- data analysis
- clustering high dimensional data
- feature space
- dimensional data
- linear discriminant analysis
- high dimensional spaces
- manifold learning
- point sets
- input space
- lower dimensional
- locally linear embedding
- clustering algorithm
- euclidean space
- shortest path
- distance metric
- clustering method
- input data
- distance measure
- nonlinear dimensionality reduction
- k means
- pattern recognition
- distance computation
- feature extraction
- cluster centroids
- decision trees