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.
Identify natural groupings of data using fuzzy c-means or subtractive clustering.
Cluster data and determine cluster centers using FCM.
Specify the crispness of the boundary between fuzzy clusters.
Cluster example numerical data using a demonstration user interface.
Interactively cluster data using fuzzy c-means or subtractive clustering.