  Given a set V of n elements on m attributes, we want to find a partition of V
on the minimum number of clusters such that the associated R-squared ratio is
at least a given threshold. We denote this problem as Goal Clustering (GC).
This problem represents a new perspective, characterizing a different
methodology within unsupervised non-hierarchical clustering. In effect, while
in the k-means we set the number of clusters in advance and then test the
associated R-squared ratio; in the GC we set an R-squared threshold lower limit
in advance and minimize k. We present two Variable Neighborhood Search (VNS)
based heuristics for the GC problem. The two heuristics use different
methodologies to start the VNS algorithms. One is based on the Ward's
construction and the other one resorts to the k-means method. Computational
tests are conducted over a set of large sized instances in order to show the
performance of the two proposed heuristics.
