(untitled) (International Journal of Engineering and Computer Science, 2017)
A frameworks 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 a priori knowledge on the applicative context. Clusters mining results are employed for understanding users’ habits and to draw their characterizing profiles. 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 PROCLUS, the well know sub clustering algorithm which is used to identify the sub spaces. The data set under analysis contains records characterized only by few features and consequently to show how to generate additional fields which describe implicit information hidden in data. Also proposed an algorithm over these two techniques for searching common patterns and regularities in order to group together users characterized by a similar profile.