65 lines
1.5 KiB
Mathematica
65 lines
1.5 KiB
Mathematica
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% Cluster 2D real-world graphs with spectral clustering and compare with k-means
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% USI, ICS, Lugano
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% Numerical Computing
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clear all;close all;
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warning OFF;
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addpath ../datasets
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addpath ../datasets/Meshes
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load airfoil1.mat
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% load barth.mat
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% load grid2.mat
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% load 3elt.mat
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% Specify the number of clusters
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K = 4;
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% Read graph
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W = Problem.A;
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Pts = Problem.aux.coord;
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n = size(Pts,1);
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% dummy var
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dummy_map = ones(n,1);
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figure;
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spy(W)
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title('Adjacency matrix')
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%% 2a) Create the Laplacian matrix and plot the graph using the 2nd and 3rd eigenvectors
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% \----------------------------/
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% Your implementation
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% \----------------------------/
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% Eigen-decomposition
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% \----------------------------/
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% Your implementation
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% \----------------------------/
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% Plot and compare
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figure;
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subplot(1,2,1);
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gplot(W,Pts)
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xlabel('Nodal coordinates')
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subplot(1,2,2);
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gplot(W,Pts)
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xlabel('TODO: Plot using Eigenvector coordinates')
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%% 2b) Cluster each graph in K = 4 clusters with the spectral and the
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% k-means method, and compare yourresults visually for each case.
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% \----------------------------/
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% Your implementation
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% \----------------------------/
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% Compare and visualize
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figure;
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subplot(1,2,1);
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gplotmap(W,Pts,dummy_map)
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title('TODO: Plot the spectral clusters')
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subplot(1,2,2);
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gplotmap(W,Pts,dummy_map)
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title('TODO: Plot the K-means clusters')
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%% 2c) Calculate the number of nodes per cluster
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[Spec_nodes,Kmeans_nodes] = USI_ClusterMetrics(K,dummy_map,dummy_map);
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