How to perform fuzzy c means clustering using custom initializations (initial fuzzy membership metric, datapoints, cluster centers.. etc)?
2 Ansichten (letzte 30 Tage)
Ältere Kommentare anzeigen
% Define the data points as coordinates (X, Y)
Data = [
1, 3;
8, 2;
1, 2;
3, 1
];
% Defining total number of clusters
NumOfClusters = 2;
% Create the initial fuzzy partition matrix (U0)
U0 = [
1, 1, 0, 0;
0, 0, 1, 1
]; % Provided initial partition matrix
% Define initial cluster center values
initial_centers = [
2.0, 2.35;
2.15, 1.9
];
% Fuzzy c-means options
fuzzy_options = [2.0];
% Perform Fuzzy C-Means clustering with initializations
[~, cluster_centers, membership] = fcm(Data', NumOfClusters, fuzzy_options, U0, [], initial_centers);
% Displaying the updated partition matrix
disp('Updated Partition Matrix (Membership):');
disp(membership);
% Plotting the clustered data with different colors for each cluster
colors = distinguishable_colors(NumOfClusters); % Generate distinct colors for clusters
figure;
for i = 1:NumOfClusters
idx = find(membership(i, :) == max(membership));
scatter(Data(idx, 1), Data(idx, 2), 100, colors(i, :), 'filled');
hold on;
end
plot(cluster_centers(:, 1), cluster_centers(:, 2), 'kx', 'MarkerSize', 15, 'LineWidth', 2); % Plot cluster centers
hold off;
% Creating a legend dynamically based on the number of clusters
legend_strings = cell(1, NumOfClusters);
for i = 1:NumOfClusters
legend_strings{i} = ['Cluster ' num2str(i)];
end
legend_strings{NumOfClusters + 1} = 'Cluster Centers';
legend(legend_strings);
title('Fuzzy C-Means Clustering with Initializations');
I want to initialize the datapoints, initial fuzzy partition matrix U0, initial_cluster centers but i am getting the following error:
Error using fcm Too many input arguments. Error in (line 28) [~, cluster_centers, membership] = fcm(Data', NumOfClusters, fuzzy_options, U0, [], initial_centers);
When i remove the initial fuzzy partition matrix U0 and initial cluster centers the code works.
0 Kommentare
Antworten (1)
Govind KM
am 2 Sep. 2024
Starting from MATLAB R2023b, Initial estimate for cluster centers can be specified by creating an “fcmOptions” object and setting the “ClusterCenters” property as follows:
Data = [1,3;
8,2;
1,2;
3,1];
NumOfClusters=2;
%Specify initial cluster centers as an C-by-N matrix,
%where C is the number of clusters and N is the number of data features.
initial_centers=[2.0, 2.35;2.15, 1.9];
%Create an fcmOptions object and set the ClusterCenters property
options = fcmOptions(NumClusters=NumOfClusters,ClusterCenters=initial_centers);
[centers,U] = fcm(Data,options);
You can also refer to this example in the documentation:
Hope this helps!
0 Kommentare
Siehe auch
Kategorien
Mehr zu Data Clustering finden Sie in Help Center und File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!