Hyperbolic SOM-based clustering of DNA fragment features for taxonomic visualization and classification.
Christian MartinNaryttza N. DiazJörg OntrupTim W. NattkemperPublished in: Bioinform. (2008)
Keyphrases
- self organizing maps
- feature vectors
- unsupervised learning
- classification accuracy
- kohonen self organizing map
- classification method
- classification models
- feature space
- high dimensionality
- feature set
- feature extraction
- classification process
- svm classifier
- k means
- som neural network
- feature maps
- support vector machine
- benchmark datasets
- additional features
- input data
- feature analysis
- supervised classification
- extracted features
- microarray data analysis
- support vector machine svm
- extracting features
- clustering algorithm
- high dimensional
- feature selection
- pattern recognition
- learning algorithm
- feature values
- unsupervised clustering
- feature reduction
- unsupervised feature selection
- neural network
- decision trees
- cluster analysis
- classification algorithm
- image classification
- kohonen self organizing maps
- discrete hidden markov models
- training data
- discriminative features
- neural gas
- data analysis
- supervised learning
- gene expression profiles
- data points
- dimensionality reduction
- input space
- clustering method
- class labels
- data clustering
- feature representation
- feature subset
- feature selection algorithms