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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 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

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