Identifying user habits through data mining on call data records. (September 2016)

• Record Type: Journal Article
• Authors: Bianchi, Filippo Maria; Rizzi, Antonello; Sadeghian, Alireza; Moiso, Corrado
• Is Part Of: Engineering applications of artificial intelligence. Volume 54(2016:Jun.)
• Page Start: 49
• Page End: 61
• Publication Date: 2016-09
• Subjects: Call data records -- Clustering -- Data mining -- Knowledge discovery -- Automatic semantic interpretation -- Frequent substructures miner -- Subspace clustering; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2016.05.007
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In this paper we propose 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. 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.