Clustering of Weighted Data
Cluster quality statistics
Plot sequences according to a fuzzy clustering.
K-Medoids or PAM clustering of weighted data.
Build a clustrange object to compare different clustering solutions.
Convert a hierarchical clustering object to a seqtree object.
Cluster Quality Indices estimation by subsampling
Share of an association between an object (described by a dissimilarit...
Compute wcKMedoids clustering for different number of clusters.
Plot of cluster quality of CLARA algorithm.
CLARA Clustering for Sequence Analysis
Automatic labeling of cluster using sequence medoids
Generate nonclustered sequence data according to different null models...
Sequence Analysis Typologies Validation Using Parametric Bootstrap
Monothetic clustering of state sequences
Aggregate identical cases.
Automatic comparison of clustering methods.
Compute the silhouette of each object using weighted data.
Clusters state sequences and weighted data. It provides an optimized weighted PAM algorithm as well as functions for aggregating replicated cases, computing cluster quality measures for a range of clustering solutions and plotting (fuzzy) clusters of state sequences. Parametric bootstraps methods to validate typology of sequences are also provided. Finally, it provides a fuzzy and crisp CLARA algorithm to cluster large database with sequence analysis.