CVAP includes 4 External validity indices, 14 Internal validity indices and 5 clustering algorithms (K-means, PAM, hierarchical clustering, SOM and etc.). It supports other clustering algorithms via loading a solution file with class labels, or by adding new codes. And similarity metrics of Euclidean distance and Pearson correlation coefficient are supported.

agg_hierarchical.m
Copyright.txt
CVAP-examples.pdf
daisy.m
daisyc.dll
datafile
........\iris-solution.txt
........\iris.txt
........\leuk72_3k.txt
data_normalization.m
find_nearpoint.m
ind2cluster.m
License_Netlab.txt
mainCVAP.fig
mainCVAP.m
mainCVAP6.fig
mainCVAP6.m
neural_gas.m
pam.m
pamc.dll
pca_of_data.m
plot_data_bylabels.m
Readme.txt
show2dim_byclass.m
similarity_euclid.m
similarity_pearson.m
similarity_pearsonC.m
som.m
somtrain.m
som_netlab.m
valid_clear_clustering.m
valid_clear_plotting.m
valid_clear_validation.m
valid_clusteringAlgs.m
valid_data_load.m
valid_data_plot.m
valid_data_plot_check.m
valid_data_split.m
valid_DbDunn.m
valid_errorate.m
valid_errorate_check.m
valid_external.m
valid_findk.m
valid_hierarchical.m
valid_internal.m
valid_internal_deviation.m
valid_internal_intra.m
valid_intrainter.m
valid_mutiple_validation.m
valid_plot_hierarchytree.m
valid_plot_index.m
valid_print_label.m
valid_redraw.m
valid_runclustering.m
valid_runvalidation.m
valid_sapcluster.m
valid_save_solution.m
valid_solution_load.m
valid_sumpearson.m
valid_sumsqures.m
valid_validation_check.m
xlim2.m

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