ClusterTools Contents

ClusterTools User Guide



Random clustering


 A Feature based dataset with M objects.
 K Vector with desired numbers of clusters, default sampling of [2:M]

 LAB M*N array with the results of the multi-level clusterings for the
 M objects. The columns refer to the N clusterings. They yield for  the objects the prototype indices of the clusters they belong to.


The dataset A with M rows (objects) is clustered by selecting a random  set of K prototypes out of A. All other objects are assigned to the  nearest prototype. In case K is a set, larger sets of prototypes will  contain the smaller ones (nested result).

The classifiers are based on the nearest neighbor classifier (KNNC) trained by the prototypes found in the clustering using KNNC. Its output  labels are the indices in A of these objects.


 randreset;                     % take care of reproducability
 data = gendatclust1(20000);    % generate 20000 objects in 10 clusters
                                % Run Mean Shift clustering
 lab = clustr(data,[2 5 10 18 30 50 100]);
                                % Show scatterplot for 10 clusters
 figure; scatn(lab(:,3),data,'Random'); 
 figure; clusteval(lab,data);   % Evaluation by active learning

See also

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

ClusterTools Contents

ClusterTools User Guide

This file has been automatically generated. If badly readable, use the help-command in Matlab.