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