Fuzzy C-Means Clustering for Iris Data
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International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research. Aug 27, 2011 Iris data set clustering. MATLAB Online Live Editor Challenge. View the winning live scripts from faculty and students who participated in the recent challenge. Download apps, toolboxes, and other File Exchange content using Add-On Explorer in MATLAB.
This example shows how to use fuzzy c-means clustering for the iris data set. This dataset was collected by botanist Edgar Anderson and contains random samples of flowers belonging to three species of iris flowers: setosa, versicolor, and virginica. For each of the species, the data set contains 50 observations for sepal length, sepal width, petal length, and petal width.
Load Data
Load the data set from the
iris.dat
data file.Partition the data into three groups named
setosa
, versicolor
, and virginica
.Plot Data in 2-D
![Matlab Matlab](/uploads/1/2/6/0/126012747/832515818.png)
The iris data contains four dimensions representing sepal length, sepal width, petal length, and petal width. Plot the data points for each combination of two dimensions.
Setup Parameters
Specify the options for clustering the data using fuzzy c-means clustering. These options are:
![Iris Dataset Matlab Download For Mac Iris Dataset Matlab Download For Mac](/uploads/1/2/6/0/126012747/235530366.png)
Nc
— Number of clusters- M — Fuzzy partition matrix exponent, which indicates the degree of fuzzy overlap between clusters. For more information, see Adjust Fuzzy Overlap in Fuzzy C-Means Clustering.
maxIter
— Maximum number of iterations. The clustering process stops after this number of iterations.minImprove
— Minimum improvement. The clustering process stops when the objective function improvement between two consecutive iterations is less than this value.
For more information about these options and the fuzzy c-means algorithm, see
fcm
.Compute Clusters
Fuzzy c-means clustering is an iterative process. Initially, the
fcm
function generates a random fuzzy partition matrix. This matrix indicates the degree of membership of each data point in each cluster.In each clustering iteration,
fcm
calculates the cluster centers and updates the fuzzy partition matrix using the calculated center locations. It then computes the objective function value.Free Downloads For Mac
Cluster the data, displaying the objective function value after each iteration.
The clustering stops when the objective function improvement is below the specified minimum threshold.
Plot the computed cluster centers as bold numbers.