If a cluster minimizes distance between points, then it is considered to be a valid cluster.
An n x 2 matrix of points
A vector of group associations
A logical value is returned indicating whether the specified groups are considered valid.
is_valid_cluster is_valid_cluster(z, groups)
With hierarchical clustering techniques it is difficult to know whether the set of clusters produced are valid. Typically this is left to interpretation and must be 'eye-balled' to choose the cutoff point as well as decide whether the cluster boundaries make sense.
For an automated system, such a manual decision point is undesirable and must be replaced by automatic process. Since this data is multidimensional, one approach is to use a distance metric or other mathematical property as a heuristic. This function uses accepts a group of clusters if the sum of the variances of the distance within each cluster is less than the variance of the distance as a single cluster.
Brian Lee Yung Rowe
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