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Data Clustering

Find clusters in input/output data using fuzzy c-means or subtractive clustering

The purpose of clustering is to identify natural groupings from a large data set to produce a concise representation of the data. You can use Fuzzy Logic Toolbox™ software to identify clusters within input/output training data using either fuzzy c-means or subtractive clustering. Also, you can use the resulting cluster information to generate a fuzzy inference system to model the data behavior. For more information, see Fuzzy Clustering.

Functions

`fcm`	Fuzzy c-means clustering
`fcmOptions`	FCM clustering options (Since R2023a)
`subclust`	Find cluster centers using subtractive clustering
`findcluster`	Open clustering tool

Topics

Fuzzy Clustering
Identify natural groupings of data using fuzzy c-means or subtractive clustering.
Fuzzy C-Means Clustering
Cluster example numerical data using a demonstration user interface.
Cluster Quasi-Random Data Using Fuzzy C-Means Clustering
Cluster data and determine cluster centers using FCM.
Adjust Fuzzy Overlap in Fuzzy C-Means Clustering
Specify the crispness of the boundary between fuzzy clusters.
Cluster Data Using Clustering Tool
Interactively cluster data using fuzzy c-means or subtractive clustering.

Featured Examples

Fuzzy C-Means Clustering for Iris Data

Classify the iris flower data set using fuzzy c-means clustering.

Open Live Script

Brain Tumor Segmentation Using Fuzzy C-Means Clustering

Segment brain tumor images using both classical and Gustafson-Kessel FCM clustering.

Open Live Script

Model Suburban Commuting Using Subtractive Clustering and ANFIS

Generate a fuzzy inference system from data using subtractive clustering.

Open Live Script