scheme using a k-means algorithm.
||Select clustering options:
- Initialization: best options will probably be with the
- Clusters: the maximum number of clusters you will get.
The program may return fewer clusters if there are not enough
distinct clusters in n-dimensional space.
- Iterations: how long to keep going
- Sampling: the clustering can use only a limited number of
points, and the data set will be thinned by this factor in both x
and y for grids, and pick every nth point for databases. The
original default should be the largest allowed. You can pick a larger value of this parameter, but not
a smaller one.
- These options can greatly slow operations.
- Scatterplots by cluster: get 2D graphs from each pair of variables,
colored by cluster.
- Scatterplots by mask:
- Histograms by cluster, colored by cluster
- Histograms by mask
- Create grid: put the classification into a grid.
- Classification distance power: the Euclidian
distance uses the the square, but other powers can also be used.
The larger the power, the greater the effect of single outliers.
Last revision 6/4/2015