Flexible Cluster Algorithms
Achievement Test Scores for New Haven Schools
Barplot/chart Methods in Package `flexclust'
Bagged Clustering
Birth and Death Rates
Bootstrap Flexclust Algorithms
German Parliament Election Data
Box-Whisker Plot Methods in Package `flexclust'
Convex Clustering
Cluster Similarity Matrix
Conversion Between S3 Partition Objects and KCCA
Dentition of Mammals
Compute Pairwise Distances Between Two Data sets
Distance and Centroid Computation
Classes "flexclustControl" and "cclustControl"
Flexclust Color Palettes
Methods for Function histogram in Package `flexclust'
Methods for Function image in Package `flexclust'
Get Information on Fitted Flexclust Objects
K-Centroids Cluster Analysis
Convert Cluster Result to Data Frame
Milk of Mammals
Nutrients in Meat, Fish and Fowl
Methods for Function pairs in Package `flexclust'
Get Centroids from KCCA Object
Methods for Function plot in Package `flexclust'
Predict Cluster Membership
Artificial 2d Market Segment Data
Add Arrows for Projected Axes to a Plot
Barcharts and Boxplots for Columns of a Data Matrix Split by Groups
Stochastic QT Clustering
Compare Partitions
Plot a Random Tour
Relabel Cluster Results.
Cluster Shadows and Silhouettes
Shadow Stars
Segment Level Stability Across Solutions Plot.
Segment Level Stability Within Solution.
Run Flexclust Algorithms Repeatedly
Stripes Plot
The main function kcca implements a general framework for k-centroids cluster analysis supporting arbitrary distance measures and centroid computation. Further cluster methods include hard competitive learning, neural gas, and QT clustering. There are numerous visualization methods for cluster results (neighborhood graphs, convex cluster hulls, barcharts of centroids, ...), and bootstrap methods for the analysis of cluster stability.