fcm

Fuzzy c-means clustering

Syntax

[centers,U] = fcm(data)

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

[centers,U,objFcn] = fcm(___)

[centers,U,objFcn,info] = fcm(___)

Description

FCM is a clustering method that allows each data point to belong to multiple clusters with varying degrees of membership. For more information, see Fuzzy C-Means Clustering.

[centers,U] = fcm(data) computes cluster centers (centers) and a fuzzy partition matrix (U) for specified data.

By default, fcm clusters the data ten times, varying the number of clusters from 2 through 11.

[centers,U] = fcm(data,options) specifies clustering options, such as the number of clusters and the distance metric.

[centers,U,objFcn] = fcm(___) returns the objective function values at each optimization iteration for the optimal number of clusters.

[centers,U,objFcn,info] = fcm(___) returns the clustering results for all numbers of clusters used along with the validity index used for determining the optimal number of clusters. When the distance metric specified in options is either "mahalanobis" or "fmle", info also contains the covariance matrices generated for each number of clusters.

Examples

Cluster Data Using Fuzzy C-Means Clustering

Load the data to cluster. Each row of fcmdata contains one data point. The two columns of fcmdata contain the feature values for each data point.

load fcmdata.dat

Specify clustering options using an fcmOptions object. For this example, set the number of clusters to 2 and use default values for the other options.

options = fcmOptions(NumClusters=2);

Find the cluster centers using fuzzy c-means clustering.

[centers,U] = fcm(fcmdata,options);

Iteration count = 1, obj. fcn = 8.97048
Iteration count = 2, obj. fcn = 7.1974
Iteration count = 3, obj. fcn = 6.32558
Iteration count = 4, obj. fcn = 4.58614
Iteration count = 5, obj. fcn = 3.89311
Iteration count = 6, obj. fcn = 3.8108
Iteration count = 7, obj. fcn = 3.7998
Iteration count = 8, obj. fcn = 3.79786
Iteration count = 9, obj. fcn = 3.79751
Iteration count = 10, obj. fcn = 3.79744
Iteration count = 11, obj. fcn = 3.79743
Iteration count = 12, obj. fcn = 3.79743
Minimum improvement reached.

Classify each data point into the cluster with the largest membership value.

maxU = max(U);
index1 = find(U(1,:) == maxU);
index2 = find(U(2,:) == maxU);

Plot the clustered data and cluster centers.

plot(fcmdata(index1,1),fcmdata(index1,2),"ob")
hold on
plot(fcmdata(index2,1),fcmdata(index2,2),"or")
plot(centers(1,1),centers(1,2),"xb",MarkerSize=15,LineWidth=3)
plot(centers(2,1),centers(2,2),"xr",MarkerSize=15,LineWidth=3)
xlabel("Feature 1")
ylabel("Feature 2")
hold off

Specify Fuzzy Overlap Between Clusters

Create a random data set.

data = rand(100,2);

Specify the following FCM clustering options.

• Compute two clusters.

• To increase the amount of fuzzy overlap between the clusters, specify a large fuzzy partition matrix exponent.

• Suppress the command-window display of the objective function values for each iteration.

options = fcmOptions(...
    NumClusters=2,...
    Exponent=3.0,...
    Verbose=false);

Cluster the data.

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

Configure Clustering Termination Conditions

Load the clustering data.

load clusterDemo.dat

Configure an options object for computing three clusters and suppress the command-window output of the objective function values. Also, set the clustering termination conditions such that the optimization stops when either of the following occurs:

• The number of iterations reaches a maximum of 50.

• The objective function improves by less than 0.001 between two consecutive iterations.

options = fcmOptions(...
    NumClusters=3,...
    MaxNumIteration=50,...
    MinImprovement=0.001,...
    Verbose=false);

Cluster the data.

[centers,U,objFcn] = fcm(clusterDemo,options);

To view the progress of the clustering, plot the objective function.

plot(objFcn)
title("Objective Function Values")   
xlabel("Iteration Count")
ylabel("Objective Function Value")

The length of the objective function vector is less than 50; therefore the clustering did not reach the maximum number of iterations.

View the final three values of the objective function vector.

objFcn(end-2:end)

ans = 3×1

   15.4353
   15.4306
   15.4305

The optimization stopped because the objective function improved by less than 0.001 between the final two iterations.

Cluster Data Using Multiple Cluster Counts

Load the data to cluster.

load clusterDemo.dat

Specify the clustering options. For this example, cluster the data three times, once each for 2, 3, and 4 clusters. Suppress the command-window output.

options = fcmOptions(...
    NumClusters=[2 3 4],...
    Verbose=false);

Cluster the data. The results returned in centers, U, and objFun correspond to the optimal number of clusters. The results for all cluster counts are returned in info.

[centers,U,objFun,info] = fcm(clusterDemo,options);

View the optimal number of clusters.

Nc = info.OptimalNumClusters

Nc = 
3

Verify the optimal number of clusters using the validity index values, which correspond to the cluster counts specified using NumClusters. The optimal number of clusters corresponds to the smallest validity index.

info.ValidityIndex

ans = 1×3

    0.3258    0.1891    5.1597

The smallest validity index corresponds to a cluster count of 3.

Plot the clustered data using the optimal clustering results. First classify each data point into the cluster with the largest membership value.

maxU = max(U);
index1 = find(U(1,:) == maxU);
index2 = find(U(2,:) == maxU);
index3 = find(U(3,:) == maxU);

Plot the clustered data and cluster centers.

figure
hold on
scatter3(clusterDemo(index1,1),clusterDemo(index1,2),...
    clusterDemo(index1,3))
scatter3(clusterDemo(index2,1),clusterDemo(index2,2),...
    clusterDemo(index2,3))
scatter3(clusterDemo(index3,1),clusterDemo(index3,2),...
    clusterDemo(index3,3))
plot3(centers(:,1),centers(:,2),centers(:,3), ...
    "xk",MarkerSize=15,LineWidth=3)
xlabel("Feature 1")
ylabel("Feature 2")
zlabel("Feature 3")
view([-11 63])
hold off

Specify Initial Estimate of Cluster Centers

Load the data to cluster.

load clusterDemo.dat

Estimate the number of clusters (Nc) and the initial cluster centers (initCenters). For this example, use the subclust function.

initCenters = subclust(clusterDemo,0.5);
Nc = size(initCenters,1);

Configure the FCM clustering to use the initial cluster centers as a starting point.

options = fcmOptions(...
    NumClusters=Nc, ...
    ClusterCenters=initCenters);

Cluster the data.

[centers,U] = fcm(clusterDemo,options);

Iteration count = 1, obj. fcn = 103.934
Iteration count = 2, obj. fcn = 15.7792
Iteration count = 3, obj. fcn = 15.7792
Minimum improvement reached.

With initial estimates of the cluster centers, the FCM algorithm converges quickly.

Input Arguments

data — Data set to be clustered

N_d-by-N_f matrix

Data set to be clustered, specified as an N_d-by-N_f matrix, where N_d is the number of data points and N_f is the number of features in each data point.

options — Clustering options

fcmOptions object

Clustering options, specified as an fcmOptions object.

Output Arguments

centers — Cluster centers

N_c-by-N_f matrix

Final cluster centers, returned as an N_c-by-N_f matrix, where N_c is the number of clusters and N_f is the number of data features. Each row contains the coordinates of one cluster center.

When options.NumClusters is a:

• Vector or "auto", N_c is the optimal number of clusters.

• Scalar, N_c is equal to options.NumClusters.

U — Fuzzy partition matrix

N_c-by-N_d matrix

Fuzzy partition matrix, returned as an N_c-by-N_d matrix, where N_c is the number of clusters and N_d is the number of data points. Element U(i,j) indicates the degree of membership μ_ij of the jth data point in the ith cluster.

When using probabilistic FCM clustering, the sum of the membership values for each cluster is one; that is, the sum of each column of U is one.

When options.NumClusters is a vector or "auto", U corresponds to the optimal number of clusters.

objFcn — Objective function values

vector

Objective function values for each clustering iteration, returned as a vector with length equal to the number of clustering iterations.

When options.NumClusters is a vector or "auto", objFcn corresponds to the optimal number of clusters.

info — Detailed clustering results

structure

Since R2023b

Detailed clustering results, returned as a structure with the following fields.

NumClusters — Number of clusters

integer | vector of integers

Number of clusters, as specified using options.NumClusters, returned as an integer or a vector of integers.

ClusterCenters — Cluster centers

cell array

Cluster centers, returned as a cell array with length equal to the length of NumClusters. The cluster centers for the optimal number of clusters are returned in centers.

Each entry in ClusterCenters is a matrix with N_c rows, where N_c is the corresponding number of clusters returned in NumClusters.

FuzzyPartitionMatrix — Fuzzy partition matrices

cell array

Fuzzy partition matrices, returned as a cell array with length equal to the length of NumClusters. Each entry in FuzzyPartitionMatrix is a matrix with N_c rows, where N_c is the corresponding number of clusters returned in NumClusters.

The partition matrix for the optimal number of clusters is returned in U.

ObjectiveFcnValue — Objective function values

cell array

Objective function values, returned as a cell array with length equal to the length of NumClusters. Each entry in ObjectiveFunctionValue is a matrix with number of rows equal to the number of clustering iterations performed for the corresponding number of clusters returned in NumClusters.

The objective function values for the optimal number of clusters are returned in objFcn.

CovarianceMatrix — Covariance matrices

cell array

Covariance matrices, returned as a cell array with length equal to the length of NumClusters.

This field is returned when options.DistanceMetric is either "mahalanobis" or "fmle".

ValidityIndex — Validity index values

scalar | vector

Validity index values, returned as a scalar or a vector with the same length as NumClusters. The minimum validity index corresponds to the optimal number of clusters. [4]

OptimalNumClusters — Optimal number of clusters

integer

Optimal number of clusters, returned as an integer. The optimal number of clusters is the value from NumClusters that corresponds to the minimum validity index in ValidityIndex.

The values returned in centers, U, and objFcn correspond to the optimal number of clusters.

Tips

• To generate a fuzzy inference system using FCM clustering, use the genfis function. For example, suppose that you cluster your data using the following syntax.

  [centers,U] = fcm(data,fcmOpt);

  The first M columns of data correspond to input variables and the remaining columns correspond to output variables.

  You can generate a fuzzy system using the same training data and FCM clustering configuration. To do so:

  1. Configure the clustering options.

     opt = genfisOptions("FCMClustering");
     opt.NumClusters = fcmOpt.NumClusters;
     opt.Exponent = fcmOpt.Exponent;
     opt.MaxNumIteration = fcmOpt.MaxNumIteration;
     opt.MinImprovement = fcmOpt.MinImprovement;
     opt.DistanceMetric = fcmOpt.DistanceMetric;
     opt.Verbose = fcmOpt.Verbose;
     

  2. Extract the input and output variable data.

     inputData = data(:,1:M);
     outputData = data(:,M+1:end);
     

  3. Generate the FIS structure.

     fis = genfis(inputData,outputData,opt);

  The fuzzy system fis contains one fuzzy rule for each cluster, and each input and output variable has one membership function per cluster. For more information, see genfis and genfisOptions.

References

[1] Bezdek, James C. Pattern Recognition with Fuzzy Objective Function Algorithms. Boston, MA: Springer US, 1981. https://doi.org/10.1007/978-1-4757-0450-1.

[2] Gustafson, Donald, and William Kessel. “Fuzzy Clustering with a Fuzzy Covariance Matrix.” In 1978 IEEE Conference on Decision and Control Including the 17th Symposium on Adaptive Processes, 761–66. San Diego, CA, USA: IEEE, 1978. https://doi.org/10.1109/CDC.1978.268028.

[3] Gath, I., and A.B. Geva. “Unsupervised Optimal Fuzzy Clustering.” IEEE Transactions on Pattern Analysis and Machine Intelligence 11, no. 7 (July 1989): 773–80. https://doi.org/10.1109/34.192473.

[4] Xie, X.L., and G. Beni. “A Validity Measure for Fuzzy Clustering.” IEEE Transactions on Pattern Analysis and Machine Intelligence 13, no. 8 (August 1991): 841–47. https://doi.org/10.1109/CDC.1978.268028.

Version History

Introduced before R2006a

R2026a: Possibilistic cluster membership values

You can now compute fuzzy clusters with possibilistic membership values, where the sum of membership values across all clusters for each data point is not constrained to one. Instead, each membership value is independent. Because removing the constraint allows for more flexible membership assignments, this method can better represent noise and outliers in data.

To perform possibilistic clustering, create an fcmOptions object and set the ClusterMembershipType property to "true".

R2026a: Specify initial cluster membership values

You can now specify an initial partition matrix containing estimated cluster membership values. Previously, the fcm function randomly initialized the partition matrix.

To specify an initial partition matrix, create an fcmOptions object and set the PartitionMatrix property.

R2023b: Specify initial cluster centers

You can now specify initial estimates of the cluster centers. Previously, the fcm function randomly initialized the cluster centers.

To specify cluster centers, create an fcmOptions object and set the ClusterCenters property.

R2023b: Specify multiple values for number of clusters

You can now specify multiple values for the number of clusters. When you do so, the fcm function finds clusters for each C value and determines the optimal number of clusters using a validity index. For more information, see Fuzzy C-Means Clustering.

To specify the number of clusters, create an fcmOptions object and set the NumClusters property.

R2023b: Compute clusters for multiple cluster counts by default

When the NumClusters property of an fcmOptions object is "auto", the fcm function now computes clusters for multiple cluster counts (2 through 11). Previously, the default number of clusters was 2.

R2023b: Gath-Geva FCM algorithm

You can now cluster data using the Gath-Geva FCM algorithm. This algorithm uses a distance metric based on fuzzy maximum-likelihood estimation.

To use this algorithm, create an fcmOptions object and set the DistanceMetric property to "fmle".

R2023a: Gustafson-Kessel FCM algorithm

You can now cluster data using the Gustafson-Kessel FCM algorithm, which allows you to detect clusters with different geometrical shapes within the same data set. This algorithm uses a Mahalanobis distance metric instead of the Euclidean distance metric used in classical FCM clustering.

To use this algorithm, create an fcmOptions object and set the DistanceMetric property to "mahalanobis".

R2023a: Specify options using fcmOptions object

To specify options for clustering data using FCM, you now use an fcmOptions object.

Previously, you specified the number of clusters using an input argument and specified other options in a vector format.

Nc = 3;
exp = 2.5;
maxIter = 200;
minImprove = 1e-4;
verbose = false;
options = [exp maxIter minImprove verbose];
[centers,U] = fcm(data,Nc,options);

Now, you specify these clustering options using an fcmOptions object.

options = fcmOptions(...
    NumClusters=Nc,...
    Exponent=exp,...
    MaxNumIteration=maxIter,...
    MinImprovement=minImprove,...
    Verbose=verbose);
[centers,U] = fcm(data,options);