  One basic requirement of many studies is the necessity of classifying data.
Clustering is a proposed method for summarizing networks. Clustering methods
can be divided into two categories named model-based approaches and algorithmic
approaches. Since the most of clustering methods depend on their input
parameters, it is important to evaluate the result of a clustering algorithm
with its different input parameters, to choose the most appropriate one. There
are several clustering validity techniques based on inner density and outer
density of clusters that represent different metrics to choose the most
appropriate clustering independent of the input parameters. According to
dependency of previous methods on the input parameters, one challenge in facing
with large systems, is to complete data incrementally that effects on the final
choice of the most appropriate clustering. Those methods define the existence
of high intensity in a cluster, and low intensity among different clusters as
the measure of choosing the optimal clustering. This measure has a tremendous
problem, not availing all data at the first stage. In this paper, we introduce
an efficient measure in which maximum number of repetitions for various initial
values occurs.
