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Data Clustering

Find clusters in input-output data using 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. Additionally, you can use the resulting cluster information to generate a Sugeno-type fuzzy inference system to model the data behavior. For more information, see Fuzzy Clustering.

Functions

fcm Fuzzy c-means clustering
subclust Find cluster centers with subtractive clustering
genfis2 Generate Fuzzy Inference System structure from data using subtractive clustering
genfis3 Generate Fuzzy Inference System structure from data using FCM clustering
findcluster Open Clustering tool

Topics

Fuzzy Clustering

Identify natural groupings of data using fuzzy c-means or subtractive clustering.

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.

Model Suburban Commuting Using Subtractive Clustering

Generate a fuzzy inference system from data using subtractive clustering.

Data Clustering Using the Clustering Tool

Interactively cluster data using FCM or subtractive clustering.

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