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 Sugeno-type fuzzy inference system to model the data behavior. For more information, see Fuzzy Clustering.

Functions

fcmFuzzy c-means clustering
subclustFind cluster centers using subtractive clustering
findclusterOpen 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.

Fuzzy C-Means Clustering

Cluster example numerical data using a demonstration user interface.

Data Clustering Using Clustering Tool

Interactively cluster data using fuzzy c-means or subtractive clustering.

Featured Examples

Fuzzy C-Means Clustering for Iris Data

Fuzzy C-Means Clustering for Iris Data

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

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Model Suburban Commuting Using Subtractive Clustering

Model Suburban Commuting Using Subtractive Clustering

Generate a fuzzy inference system from data using subtractive clustering.

Open Live Script
Modeling Traffic Patterns using Subtractive Clustering

Modeling Traffic Patterns using Subtractive Clustering

Generate a fuzzy inference system from data using subtractive clustering.

Open Script

Fuzzy Logic Toolbox Documentation

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