Code covered by the BSD License
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SystemEvolution_energy(labels...
Energy computations for System Evolution to Estimate the Number of Clusters
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SystemEvolution_findk(inter,i...
(2) collecting partition and merge energies gained
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[A1,A2,B1,B2,A3,B3]=cluster_m...
Two-cluster model and twin-clusters
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[A2,B2]=findnear(Dist,A1,na,B...
finding nearest elements between two groups A1 and B1
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[Ra,na]=border_points(Dm,A1,B...
finding out nearest points between two groups A1 and B1
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[S,T,singl,simter,kmin]=close...
finding such a border region of a cluster that is nearest another cluster
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[ft,fd]=plotindice(ind,N1,N2,...
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[inter,intra]=energy_euclid(D...
computing energies (average walking distance) within each group A1,B1,...
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[inter,intra]=energy_pearson(...
computing energies (average walking distance) within each group A1,B1,...
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[pcfirst,eigvect,eigval,pcord...
extracting PCA components from data (along dimensions)
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daisy(x,vtype,metric)
DAISY returns a matrix containing all the pairwise dissimilarities
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data_load(id,sw,nk,N2,type)
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energy_compute(Dist,A1,A2,B1,...
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energydiff_allclusters(Dist,l...
finding energies for all clusters
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ind2cluster(labels)
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nearest(z,xi);
NEAREST: return the vector zj in z that is nearest to xi
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pam(x,kclus,vtype,stdize,metr...
PAM returns a list representing a clustering of the data into kclus
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pam_running(data,N,Ns,type,st...
calculating dis-similarity/distance matrix of a data set
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plot_evolution_route(data,lab...
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plotdata_bylabels(data,classl...
plotting data in first two principle components (pc) or dimensions
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regionF_find(Dmatrix,A1,B1,di...
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remove_closest(Dm,Ra,Rb)
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remove_outlier(Dmatrix,A1,nA)
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show2dim_byclass(davec,clas,i...
showing 2-dimensional data in X-Y space
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showClass_up(data, labels, hi...
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similarity_euclid(data)
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similarity_pearson(data)
pearson coefficients between every two columns
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standarz(data)
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valid_errorate(labels,truelab...
computing error rates for every clusters if true labels are given
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valid_external(index1,c2)
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walk_distance(Dmatrix,A,k,ch)
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walk_route(DM, A1, startpoint...
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xlim(arg1, arg2)
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MainSystemEvolution.m
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datasort_bylabel.m
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View all files
Estimating the number of clusters via System Evolution
by Kaijun Wang
07 Jul 2009
estimate number of clusters for far clusters, small-larger clusters, slightly overlapping clusters
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| File Information |
| Description |
(SE) is developed to estimate the number of clusters (NC) for PAM clustering algorithm. By inspecting whether it is stable that two potential clusters are separated or merged, SE focuses on the separability of two closest clusters among k potential clusters (called twin-clusters) when a data set is divided to k clusters.
SE judges the separability of far clusters, clusters with small clusters near larger clusters or slightly overlapping between clusters. |
| Required Products |
Statistics Toolbox
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| MATLAB release |
MATLAB 7.2 (R2006a)
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