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Appl. Sci. 2017, 7(8), 798; doi:10.3390/app7080798

Real-Time Recognition of Calling Pattern and Behaviour of Mobile Phone Users through Anomaly Detection and Dynamically-Evolving Clustering

1
Computer Science Department, Carlos III University of Madrid, Leganés, Madrid 28918, Spain
2
Computing and Communications Department, Lancaster University, Lancaster LA14WA, UK
*
Author to whom correspondence should be addressed.
Received: 31 July 2017 / Revised: 2 August 2017 / Accepted: 2 August 2017 / Published: 5 August 2017
(This article belongs to the Special Issue Human Activity Recognition)
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Abstract

In the competitive telecommunications market, the information that the mobile telecom operators can obtain by regularly analysing their massive stored call logs, is of great interest. Although the data that can be extracted nowadays from mobile phones have been enriched with much information, the data solely from the call logs can give us vital information about the customers. This information is usually related with the calling behaviour of their customers and it can be used to manage them. However, the analysis of these data is normally very complex because of the vast data stream to analyse. Thus, efficient data mining techniques need to be used for this purpose. In this paper, a novel approach to analyse call detail records (CDR) is proposed, with the main goal to extract and cluster different calling patterns or behaviours, and to detect outliers. The main novelty of this approach is that it works in real-time using an evolving and recursive framework. View Full-Text
Keywords: human activity recognition; evolving systems; analysing calling behaviour; detecting outliers; clustering human activity recognition; evolving systems; analysing calling behaviour; detecting outliers; clustering
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Iglesias, J.A.; Ledezma, A.; Sanchis, A.; Angelov, P. Real-Time Recognition of Calling Pattern and Behaviour of Mobile Phone Users through Anomaly Detection and Dynamically-Evolving Clustering. Appl. Sci. 2017, 7, 798.

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