ClusterTools Contents

ClusterTools User Guide



Cluster classification error on indiviual clusterings


 LABC Index array, size [M,K], indices of cluster prototypes for M objects in K clusterings.
 LABT Vector of M elements with true object labels.  Default: true labels used in some other routine.
 A PRTools labeled dataset used for the clustering.

 E Vector with classification errors of the clusterings in LABC.
 N Vector with number of clusters per clustering.


This routine evaluates a clustering of a dataset A by comparing the true  labels LABT of A with cluster labels derived from a cluster prototype  object. This can be understood as evaluating the clusterings by active  labeling.

A is a labeled dataset with M objects. LABT is a vertical vector  containing the true numeric labels of A. LABT = GETNLAB(A). LABC is a set  of K clustering results with indices pointing for every object to a  cluster prototype in A.

E is the fraction of misclassified objects in A if every cluster is  assigned to the class of the object indicated by LABC. This is based on a  training set of N prototypes. The classification error E is based on all  objects, including the prototypes.

In case LABC is an MxK result of a multilevel clustering, E and N are  vectors with K elements. Use MCLUSTCERR for a classification result  that combines clusterings.


 randreset; % take care of reproducability
 A = gendat(mnist8,25000);
 randreset; labc1 = A*clustm(false);   % no nesting
 randreset; labc2 = A*clustm;          % nesting
 [e1,n1] = clustcerr(labc1,A);
 [e2,n2] = clustcerr(labc2,A);
 semilogx(n1,e1); hold on
 title(['Active learning curve: ' getname(A)])
 xlabel('Training set size - number of clusters')
 ylabel('Classification error')

See also

datasets, mappings, knnc, cluste, clusth, clustk, clustkh, clustm, clustf, clustr, dcluste, dclustf, dclusth, dclustk, dclustm, dclustr, clusteval, clustc, clustnum, clustlcurve,

ClusterTools Contents

ClusterTools User Guide

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