K-Means

K-MEANS
Parameters	values	Definition
Kernel Type	0. LINEAR	linear: u'*v
	1. POLYNOMIAL	polynomial: (gammau'v + coef0)^degree
	2. RBF	radial basis function: exp(-gamma*\|u-v\|^2) This kernel nonlinearly maps samples into a higher dimensional space so it, unlike the linear kernel, can handle the case when the relation between className labels and attributes is nonlinear.
	3. SIGMOID	sigmoid: tanh(gammau'v + coef0)
Gamma		gamma defines how much influence a single training example has. The larger gamma is, the closer other examples must be to be affected.
Coef0		Degree of the polynomial kernel function. Ignored by all other kernels.
Degree		Independent term in kernel function. It is only significant in 'polynomial' and 'sigmoid'.
Dimension (Number of Attributes)		Number of input attributes / columns in the training data set
Number of Centers		Number of clusters
Stopping Criteria		Tolerance for stopping criterion. The stopping tolerance affects the number of iterations used when optimizing the model.
Number of Rows		Total number of records / rows in the training data

K-MEANS