Matlab实现模糊聚类Kmeans算法详解

function varargout = kmeans(X, k, varargin)
%KMEANS K-means clustering.
%   IDX = KMEANS(X, K) partitions the points in the N-by-P data matrix X
%   into K clusters.  This partition minimizes the sum, over all clusters, of
%   the within-cluster sums of point-to-cluster-centroid distances.  Rows of X
%   correspond to points, columns correspond to variables.  Note: when X is a
%   vector, KMEANS treats it as an N-by-1 data matrix, regardless of its
%   orientation.  KMEANS returns an N-by-1 vector IDX containing the cluster
%   indices of each point.  By default, KMEANS uses squared Euclidean
%   distances.
%
%   KMEANS treats NaNs as missing data, and ignores any rows of X that
%   contain NaNs.
%
%   [IDX, C] = KMEANS(X, K) returns the K cluster centroid locations in
%   the K-by-P matrix C.
%
%   [IDX, C, SUMD] = KMEANS(X, K) returns the within-cluster sums of
%   point-to-centroid distances in the K-by-1 vector sumD.
%
%   [IDX, C, SUMD, D] = KMEANS(X, K) returns distances from each point
%   to every centroid in the N-by-K matrix D.
%
%   [ ... ] = KMEANS(..., 'PARAM1',val1, 'PARAM2',val2, ...) specifies
%   optional parameter name/value pairs to control the iterative algorithm
%   used by KMEANS.  Parameters are:
%
%   'Distance' - Distance measure, in P-dimensional space, that KMEANS
%      should minimize with respect to.  Choices are:
%          'sqeuclidean'  - Squared Euclidean distance (the default)

%          'cityblock'    - Sum of absolute differences, a.k.a. L1 distance
%          'cosine'       - One minus the cosine of the included angle
%                           between points (treated as vectors)
%          'correlation'  - One minus the sample correlation between points
%                           (treated as sequences of values)
%          'hamming'      - Percentage of bits that differ (only suitable
%                           for binary data)
%
%   'Start' - Method used to choose initial cluster centroid positions,
%      sometimes known as "seeds".  Choices are:
%          'plus'    - The Default. Select K observations from X according
%                      to the k-means++ algorithm: the first cluster center
%                      is chosen uniformly at random from X, after which
%                      each subsequent cluster center is chosen randomly
%                      from the remaining data points with probability
%                      proportional to its distance from the point's
%                      closest existing cluster center.
%          'sample'  - Select K observations from X at random.
%          'uniform' - Select K points uniformly at random from the range
%                      of X.  Not valid for Hamming distance.
%          'cluster' - Perform preliminary clustering phase on random 10%
%                      subsample of X.  This preliminary phase is itself
%                      initialized using 'sample'.
%           matrix   - A K-by-P matrix of starting locations.  In this case,
%                      you can pass in [] for K, and KMEANS infers K from
%                      the first dimension of the matrix.  You can also
%                      supply a 3D array, implying a value for 'Replicates'
%                      from the array's third dimension.
%
%   'Replicates' - Number of times to repeat the clustering, each with a