Fuzzy C-means clustering algorithm can cluster the input data set into the specified cluster_n class.

functional description

Grammatical format

[center, u, obj_fcn] = FCM (data, clustering _n, option)

Usage:

1.[center，U，obj_fcn] = FCM(Data，N_cluster，options)；

2.[center，U，obj_fcn] = FCM(Data，N _ cluster)；

Input variable

Data-n * m matrix, representing n samples, each sample has m-dimensional eigenvalue.

Cluster_n-scalar, indicating the number of aggregation centers, that is, the number of categories.

Option -4 * 1 column vector, where

Options( 1): the index of the membership matrix u, >; 1 (default: 2.0)

Option (2): Maximum number of iterations (default: 100)

Option (3): Minimum change of membership, iteration termination condition (default value: 1e-5).

Options(4): Whether to output information flag for each iteration (default: 0).

Output variable

Center-cluster center

U-membership matrix

Objective function value