DBSCAN algorithm and Rand index

Hi,

I used DBSCAN and clustered a dataset, As known, result clusters have a noise cluster.Then I want to estimate Rand index on result cluster. But I have noise cluster ,There is not a response of noise points in contingency table. how can I use noise cluster for estimate contingency table ? Whether I ignore noise result or not ?

Answer 1

[Marco Riani]

Function RandIndexFS of the FSDA toolbox does what you asked.

For a preview of the documentation of this function please see

http://rosa.unipr.it/FSDA/RandIndexFS.html

Below there is an example of the use of this function

load fisheriris
% first partition c1 is the true partition
c1=species;
% second partition c2 is the output of tclust clustering procedure
k=3;
out=tclust(meas,k,0.1,100,'msg',0);
c2=out.idx;
% Units inside c2 which contain number 0 are referred to trimmed observations
% With option noisecluster you can decide to ignore or not to ignore
% noise cluster
noisecluster=0;
[ARI,RI,MI,HI]=RandIndexFS(c1,c2,noisecluster);
disp("adjusted ARI index ignoring noise cluster")
disp(ARI)

% Contingency table
[tbl,~,~,labels]=crosstab(c1,c2);

TBL=array2table(tbl,'RowNames',labels(1:3,1),'VariableNames',labels(1:k+1,2));
disp(TBL)

Hope it helps

Marco