# How to apply Silhouette Score for optimal K in MATLAB

9 views (last 30 days)
Med Future on 10 Jan 2023
Commented: Walter Roberson on 12 Jan 2023
Hello,I Hope you are doing well. I have the following dataset i want to apply Silhouette Score from scrath.
I have the Python Script for that. Can anybody help me in implmenting it into MATLAB
The following code is in Python
costs = []
for p in range(10):
kmeans = K_Means(k=p,data = data,centeriod_init='random')
centroids, cluster_assignments, iters, orig_centroids = kmeans.fit(data)
X = data
dist_ji = 0
a = 0
s=0
for i in range(len(data)):
for j in range(p):
dist_ji += euclidean_dist(centroids[j,:],X[i,:])
#print(dist_ji)
dist_ji -= sum(cluster_assignments[:,1])/len(data)
a = sum(cluster_assignments[:,1])/(len(data)-1)
s = (dist_ji - a)/max(dist_ji,a)
s = np.array(s)
s = s.item()
costs.append(s)
x = np.arange(10)
plt.plot(x,costs)
plt.title("Silhoutte Score")
plt.xlabel("K -->")
plt.ylabel("Dispersion")
Med Future on 11 Jan 2023

Walter Roberson on 11 Jan 2023
I do not know enough python to know how to convert this code.
I suspect: call kmeans() with p as the number of centroids, getting back indices and centroid locations. Then take
nearest_center = CentroidLocations(CentroidIdx,:);
a = mean((data - nearest_center).^2,2);
or something like that.
Walter Roberson on 12 Jan 2023
N = 10;
s = zeros(size(data,1), N);
for p = 1 : N
[CentroidIdx, CentroidLocations] = kmeans(data, p); %random initialization is default
nearest_center = CentroidLocations(CentroidIdx,:);
dist_ji = sum((data - nearest_center).^2,2);
a = mean(dist_ji);
s(:,p) = (dist_ji - a)./max(dist_ji,a);
end
plot(s)

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