dbscan的matlab代码实现

1、%Function:class,type=dbscan(x,k,Eps)%Aim:%ClusteringthedatawithDensity-BasedScanAlgorithmwithNoise(DBSCAN)%Input:%x-dataset(m,n);m-objects,n-variables%k-numberofobjectsinaneighborhoodofanobject%(minimalnumberofobjectsconsideredasacluster)%Eps-neighborhoodradius,ifnotknownavoidthisparameterorput%Outp

2、ut:%class-vectorspecifyingassignmentofthei-thobjecttocertain%cluster(m,1)%type-vectorspecifyingtypeofthei-thobject%(core:1,border:0,outlier:-1)%Exampleofuse:%x=randn(30,2)*.4;randn(40,2)*.5+ones(40,1)*44;%class,type=dbscan(x,5,)%clusteringfigs(Dbscan,x,12,class,type)%References:%1M.Ester,H.Kriegel,J

3、.Sander,X.Xu,Adensity-basedalgorithmfor%discoveringclustersinlargespatialdatabaseswithnoise,proc.%2ndInt.Conf.onKnowledgeDiscoveryandDataMining,Portland,OR,1996,%p.226,availablefrom:%rmatik.uni-muenchen.de/cgi-bin/papers?query=-CO%2M.Daszykowski,B.Walczak,D.L.Massart,Lookingfor%NaturalPa

4、tternsinData.Part1:DensityBasedApproach,%Chemom.Intell.Lab.Syst.56(2001)83-92%WrittenbyMichalDaszykowski%DepartmentofChemometrics,InstituteofChemistry,%TheUniversityofSilesia%December2004%.plfunctionclass,type,clusteridx=clu_dbscan_fn(x,k,Eps)x=zscore(x);%standarlizem,=si

5、ze(x);ifnargin3|isempty(Eps)Eps=epsilon(x,k);endx=(1:m),x;m,n=size(x);type=zeros(1,m);no=1;touched=zeros(m,1);fori=1:miftouched(i)=0;ob=x(i,:);D=dist(ob(2:n),x(:,2:n);ind=find(D1&length(ind)=k+1;type(i)=1;class(ind)=ones(length(ind),1)*max(no);whileisempty(ind)ob=x(ind(1),:);touched(ind(1)=1;ind(1)=

6、;D=dist(ob(2:n),x(:,2:n);i1=find(D1class(i1)=no;iflength(i1)=k+1;type(ob(1)=1;elsetype(ob(1)=0;endfork1=1:length(i1)iftouched(i1(k1)=0touched(i1(k1)=1;ind=ind,i1(k1);class(i1(k1)=no;endendendendno=no+1;endendendi1=find(class=0);class(i1)=-1;type(i1)=-1;maxlab=max(class);clusteridx=;clun=;forck=1:max

7、labtidx=find(class=ck);clusteridx=clusteridx;tidx,zeros(1,m-length(tidx);clun=clun,length(tidx);enddisp(clun);%functionEps=epsilon(x,k)%Function:Eps=epsilon(x,k)%Aim:%AnalyticalwayofestimatingneighborhoodradiusforDBSCAN%Input:%x-datamatrix(m,n);m-objects,n-variables%k-numberofobjectsinaneighborhoodo

8、fanobject%(minimalnumberofobjectsconsideredasacluster)m,n=size(x);Eps=(prod(max(x)-min(x)*k*gamma(.5*n+l)/(m*sqrt(pi.An).人(1/n);disp(EPS:);disp(Eps);%functionD=dist(i,x)%function:D=dist(i,x)%Aim:%CalculatestheEuclideandistancesbetweenthei-thobjectandallobjectsinx%Input:%i-anobject(1,n)%x-datamatrix(m,n);