k-Means Expert Options

Iterations. Sets the number of iterations for the k-Means algorithm.

Convergence tolerance. Sets the convergence tolerance for the k-Means algorithm.

Number of bins. Specifies the number of bins in the attribute histogram produced by k-Means. The bin boundaries for each attribute are computed globally on the entire training dataset. The binning method is equi-width. All attributes have the same number of bins with the exception of attributes with a single value that have only one bin.

Block growth. Sets the growth factor for memory allocated to hold cluster data.

Minimum Percent Attribute Support. Sets the fraction of attribute values that must be non-null in order for the attribute to be included in the rule description for the cluster. Setting the parameter value too high in data with missing values can result in very short or even empty rules.