A comparative study of dimensionality reduction techniques to enhance trace clustering performances.
Minseok SongH. YangSeyed Hossein SiadatMykola PechenizkiyPublished in: Expert Syst. Appl. (2013)
Keyphrases
- dimensionality reduction
- high dimensional data
- high dimensionality
- unsupervised learning
- data points
- clustering algorithm
- pattern recognition and machine learning
- low dimensional
- k means
- clustering method
- principal component analysis
- multidimensional data
- categorical data
- principal components analysis
- principal components
- linear discriminant analysis
- feature extraction
- pattern recognition
- cluster analysis
- dimension reduction
- nonlinear dimensionality reduction
- hierarchical clustering
- multidimensional scaling
- feature selection
- input space
- high dimensional
- dealing with high dimensional data
- graph theoretic
- unsupervised feature selection
- structure preserving
- random projections
- subspace clustering
- manifold learning
- fuzzy clustering
- spectral clustering
- data clustering
- distance metric
- outlier detection
- feature space