Martin, Christian: Visual data mining in intrinsic hierarchical complex biodata. 2009
Inhalt
- Introduction
- Machine learning algorithms
- Hierarchical Agglomerative Clustering
- Spectral Clustering
- Self-Organizing Maps
- Topology Preservation for SOMs
- Topographic error
- Quantification error and distortion
- Trustworthiness and Discontinuities
- Measures based on correlations of distances
- k-nearest neighbor classifier
- Data
- Cluster Validation
- Internal cluster indices
- intra- and inter-cluster variance
- Calinski Harabasz Index
- Index I
- Separation
- Silhouette Width
- Davis-Bouldin index
- Dunn's index
- C Index
- Goodman-Kruskal Index
- External cluster indices
- Cluster validation bias
- Stability of clustering results
- The Tree Index
- Methods
- Results
- Theoretical considerations
- Tree structures and leaf orderings
- Different scoring methodologies
- The probability of a split
- Cumulative hypergeometric distribution
- Discussion
- Normalized Tree Index
- Fusing biomedical multi-modal data
- Taxonomic classification of DNA fragments
- Reassessing the tree of life
- Conclusion