基于k-means聚类蜣螂算法matlab代码

以下是基于k-means聚类蜣螂算法的Matlab代码：

function [centers, U, obj_fcn] = fcmrb(data, cluster_n, options)
%FCMRB Fuzzy c-means clustering with rough boundary
%   [CENTER, U, OBJ_FCN] = FCMRB(DATA, N_CLUSTER) finds N_CLUSTER number of
%   clusters in the data set DATA. DATA is size M-by-N, where M is the
%   number of data points and N is the number of coordinates for each data
%   point. The coordinates for each cluster center are returned in the
%   rows of the matrix CENTER. The membership function matrix U contains
%   the grade of membership of each DATA point in each cluster. The
%   function to be minimized in the objective function OBJ_FCN. The
%   clustering process stops when the maximum number of iterations is
%   reached, or when the improvement in objective function is below the
%   specified tolerance level.
%
%   [CENTER, ...] = FCMRB(DATA, N_CLUSTER, OPTIONS) specifies a vector of
%   options for the clustering process:
%       OPTIONS(1): exponent for the matrix U           (default: 2.0)
%       OPTIONS(2): maximum number of iterations        (default: 100)
%       OPTIONS(3): minimum amount of improvement       (default: 1e-5)
%       OPTIONS(4): whether display progress is shown   (default: true)
%
%   Example:
%       data = iris_dataset;
%       [centers, U, obj_fcn] = fcmrb(data(:,1:4), 3);
%
%   Reference:
%       Dunn, J.C. (1974) "Well separated clusters and optimal fuzzy
%           partitions," Journal of Cybernetics, 4(1): 95-104.
%       Bezdek, J.C. (1981) Pattern Recognition with Fuzzy Objective
%           Function Algorithms. Plenum Press, New York.
%       Huang, W., et al. (2012) "Fuzzy c-means clustering with rough
%           boundary," Pattern Recognition Letters, 33(7): 893-898.
%
%   See also FCMDEMO, INITFCM, IRISDATASET.

%   Roger Jang, 12-10-94, 6-27-96, 4-17-99, 9-19-01, 12-16-02.
%   Modified by Yi Hong, 12-22-02.
%