In MATLAB, the Fuzzy C-Means (FCM) algorithm is available through the `fcm` function in the Fuzzy Logic Toolbox. By default, this function uses the Euclidean distance metric for clustering data points. However, you can modify the distance metric and other options by providing additional arguments to the `fcm` function.

The syntax of the `fcm` function is as follows:

[centers, U] = fcm(data, num_clusters, options)

Where:

- `data`: Input data matrix, where each row represents a data point and each column represents a feature.

- `num_clusters`: The desired number of clusters to be formed.

- `options`: An optional argument that allows you to set various options for the FCM algorithm. This argument is a struct that can contain various fields to modify the behavior of the algorithm.

To specify a different distance metric, you can use the `'distance'` field in the `options` struct. For example, if you want to use the city-block (Manhattan) distance, you can do the following:

% Load your data from the .xlsx file (assuming the data is stored in sheet1)

data = xlsread('data_file.xlsx', 'Sheet1');

% Set the number of clusters you want to form

num_clusters = 3;

% Create the options struct with the desired distance metric

options = struct('distance', 'cityblock'); % 'cityblock' for Manhattan distance

% Run the FCM algorithm

[centers, U] = fcm(data, num_clusters, options);

In the above example, we set the `'distance'` field of the `options` struct to `'cityblock'`, which specifies that the FCM algorithm should use the city-block distance metric. Similarly, you can use other distance metrics available in MATLAB, such as `'cosine'` or `'correlation'`, depending on your specific requirements.

Additionally, the `options` struct can contain other fields to modify convergence criteria, maximum iterations, and more. You can refer to MATLAB's documentation on the `fcm` function for a complete list of available options and their usage.

Thank you very much for the answer

However, this option was dealt with and the program was not executed because matlab is installed by default with two options for calculating the distance when using the fuzzy cluster algorithm

Thank you very much for the answer

But these options were implemented and fcmOpt.DistanceMetric did not accept the change

Exactly, this is what I reached for the purpose of calculating the distances, but I wanted to discuss this topic, perhaps there is a way to address the case.

With sincere regards