  In this paper we propose a framework for identifying patterns and
regularities in the pseudo-anonymized Call Data Records (CDR) pertaining a
generic subscriber of a mobile operator. We face the challenging task of
automatically deriving meaningful information from the available data, by using
an unsupervised procedure of cluster analysis and without including in the
model any \textit{a-priori} knowledge on the applicative context. Clusters
mining results are employed for understanding users' habits and to draw their
characterizing profiles. We propose two implementations of the data mining
procedure; the first is based on a novel system for clusters and knowledge
discovery called LD-ABCD, capable of retrieving clusters and, at the same time,
to automatically discover for each returned cluster the most appropriate
dissimilarity measure (local metric). The second approach instead is based on
PROCLUS, the well-know subclustering algorithm. The dataset under analysis
contains records characterized only by few features and, consequently, we show
how to generate additional fields which describe implicit information hidden in
data. Finally, we propose an effective graphical representation of the results
of the data-mining procedure, which can be easily understood and employed by
analysts for practical applications.
