The main goal of data mining process is to extract information and
discover knowledge from huge databases, where the clustering is
one of the most important functionalities which can be done in this
area. There are many of clustering algorithms an
d methods, but
determining or estimating the number of clusters which should be
extracted from a dataset is one of the most important issues most of
these methods encounter it. This research focuses on the problem of
estimating number of clusters in the case of agglomerative
hierarchical clustering. We present an evaluation of three of the
most common methods used in estimating number of clusters.
