Explorative hyperbolic-tree-based clustering tool for unsupervised knowledge discovery.
Michael RieglerKonstantin PogorelovMathias LuxPål HalvorsenCarsten GriwodzThomas de LangeSigrun Losada EskelandPublished in: CBMI (2016)
Keyphrases
- knowledge discovery
- unsupervised learning
- clustering algorithm
- unsupervised classification
- conceptual clustering
- k means
- information bottleneck
- clustering method
- data clustering
- outlier detection
- unsupervised manner
- cluster validation
- supervised learning
- data mining and knowledge discovery
- unsupervised feature selection
- agglomerative clustering
- supervised classification
- unsupervised clustering
- data mining tasks
- cluster analysis
- self organizing maps
- software tools
- concept lattice
- data mining tools
- databases
- completely unsupervised
- data visualization
- hierarchical clustering
- document clustering
- neural network
- formal concept analysis
- graph partitioning
- fuzzy c means
- normalized cut
- fuzzy clustering
- information theoretic
- semi supervised
- association rules
- data mining
- database