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data clustering -凯发k8网页登录

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 fuzzy inference system to model the data behavior. for more information, see .

functions

fuzzy c-means clustering
fcm clustering options
find cluster centers using subtractive clustering
open clustering tool

topics


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


  • cluster example numerical data using a demonstration user interface.


  • cluster data and determine cluster centers using fcm.


  • specify the crispness of the boundary between fuzzy clusters.


  • interactively cluster data using fuzzy c-means or subtractive clustering.

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