Using the Clustering tool, you can cluster data using fuzzy c-means or subtractive clustering For more information on the clustering methods, see Fuzzy Clustering.
To open the tool, at the MATLAB® command line, type:
Use the Clustering tool to perform the following tasks:
Load and plot the data.
Perform the clustering.
Save the cluster center.
Access the online help topics by clicking Info or using the Help menu.
To load a data set, perform either of the following actions:
Click Load Data, and select the file containing the data.
Open the Clustering Tool with a data set directly by calling
findcluster with the data set
as an input argument.
For example, enter:
The data set file must have the extension
.dat. Each line of
the data set file contains one data point. For example, if you have 5-dimensional
data with 100 data points, the file contains 100 lines, and each line contains five
The Clustering tool works on multidimensional data sets, but displays only two of those dimensions on the plot. To select other dimensions in the data set for plotting, you can use the drop-down lists under X-axis and Y-axis.
To start clustering the data:
Choose the clustering function
C-Means clustering) or
(subtractive clustering) from the drop-down menu under
Set options for:
Fuzzy c-means clustering using the Cluster
Num, Max Iteration,
Min, and Exponent
fields. For information on these options, see
Subtractive clustering using the Influence
Aspect Ratio, and Reject
Ratio fields. To use a different influence range
for each data column, specify Influence
Range as a vector with the number of elements
equal to the number of columns. For information on these
Cluster the data by clicking Start.
Once the clustering is complete, the cluster centers appear in black.
Using the Clustering tool, you can obtain only the computed cluster centers. To obtain additional information for:
To use the same clustering data with either
subclust, first load the data file into the MATLAB workspace. For example, at the MATLAB command line, type:
To save the cluster centers, click Save Center.