28-04-2017, 11:02 AM
Numerical data grouping forms the basis of many system classification and modeling algorithms. The purpose of clustering is to identify natural clusters of data from a large dataset to produce a concise representation of the behavior of a system.
Fuzzy c-means (FCM) is a technique of grouping data in which a dataset is grouped into n clusters with each data point in the dataset belonging to each group to some degree. For example, a particular data point that is near the center of a cluster will have a high degree of membership or membership in that cluster and another data point that is far from the center of a cluster will have a low degree of membership or membership To that group.
The Fuzzy Logic Toolbox ™ fcm performs FCM collation. It begins with an initial guess for cluster centers, which are meant to mark the average location of each cluster. The initial guess for these cluster centers is most likely incorrect. Then, fcm assigns each membership point a degree of membership for each cluster. When iteratively updates the cluster centers and membership degrees for each data point, fcm iteratively moves the cluster centers to the correct location within a dataset. This iteration is based on minimizing an objective function representing the distance from any given data point to a cluster center weighted by the degree of membership of that data point.
Fuzzy c-means (FCM) is a technique of grouping data in which a dataset is grouped into n clusters with each data point in the dataset belonging to each group to some degree. For example, a particular data point that is near the center of a cluster will have a high degree of membership or membership in that cluster and another data point that is far from the center of a cluster will have a low degree of membership or membership To that group.
The Fuzzy Logic Toolbox ™ fcm performs FCM collation. It begins with an initial guess for cluster centers, which are meant to mark the average location of each cluster. The initial guess for these cluster centers is most likely incorrect. Then, fcm assigns each membership point a degree of membership for each cluster. When iteratively updates the cluster centers and membership degrees for each data point, fcm iteratively moves the cluster centers to the correct location within a dataset. This iteration is based on minimizing an objective function representing the distance from any given data point to a cluster center weighted by the degree of membership of that data point.