  An important issue in clustering concerns the avoidance of false positives
while searching for clusters. This work addressed this problem considering
agglomerative methods, namely single, average, median, complete, centroid and
Ward's approaches applied to unimodal and bimodal datasets obeying uniform,
gaussian, exponential and power-law distributions. A model of clusters was also
adopted, involving a higher density nucleus surrounded by a transition,
followed by outliers. This paved the way to defining an objective means for
identifying the clusters from dendrograms. The adopted model also allowed the
relevance of the clusters to be quantified in terms of the height of their
subtrees. The obtained results include the verification that many methods
detect two clusters in unimodal data. The single-linkage method was found to be
more resilient to false positives. Also, several methods detected clusters not
corresponding directly to the nucleus. The possibility of identifying the type
of distribution was also investigated.
