Similarity Network Fusion
Affinity matrix calculation
Mutual Information calculation
Pairwise Chi-squared distances
Concordance Network NMI calculation
Plot given similarity matrix by clusters
Display the similarity matrix by clusters with some sample information
Pairwise squared Euclidean distances
Estimate Number Of Clusters Given Graph
Obtaining a vector of colors from a numeric vector of group
Group Predict
Display heatmap for clusters
Internal SNFtool Functions
Plot Alluvial
Rank Features by NMI
Similarity Network Fusion
Spectral Clustering
Standard Normalization
Similarity Network Fusion takes multiple views of a network and fuses them together to construct an overall status matrix. The input to our algorithm can be feature vectors, pairwise distances, or pairwise similarities. The learned status matrix can then be used for retrieval, clustering, and classification.