Function Index
	KMeans KMeans[data, seeds] returns a clustering of data by minimizing the within-cluster sums-of-squares, given initial estimates for the cluster centers, seeds. KMeans[data, seeds, weights] returns a clustering of data by minimizing the within-cluster sums-of-squares, given initial estimates for the cluster centers, seeds, and weights for each element of the measurement vectors in data. The argument data is a list of n-element measurement vectors, seeds is a list of n-element vectors denoting the initial estimates of the cluster centers, and weights{,} if given, is an n-element vector. The length of the argument seeds determines the number of clusters returned by KMeans. The default option DistanceFunction→SqrtDistance selects the Euclidean distance metric for use in KMeans. Other distance functions may lead to different clustering results. The following specifies the Minkowski metric: DistanceFunction → (MinkowskiDistance[#1, #2, 1/2]&). The option MaxRecursion → 15 limits the maximum number of iterations of the algorithm. See also User's Guide 7.5. Example This loads the package. In[1]:= This defines three seeds. In[2]:= This generates the example data. In[3]:= Here is a scatter plot of the data. In[5]:= This finds a grouping of the data that minimizes the within-cluster Euclidean distance. In[6]:= This plots the result using different colors for each of the clusters. In[7]:= In[9]:=