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

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Real-time Recognition of Calling Pattern and Behaviour of Mobile Phone users through Anomaly Detection and Dynamically Evolving Clustering. / Iglesias, Jose; Ledezma, Agapito; Sanchis, Araceli et al.
In: Applied Sciences, Vol. 7, No. 8, 798, 05.08.2017, p. 1-14.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Iglesias, J, Ledezma, A, Sanchis, A & Angelov, PP 2017, 'Real-time Recognition of Calling Pattern and Behaviour of Mobile Phone users through Anomaly Detection and Dynamically Evolving Clustering', Applied Sciences, vol. 7, no. 8, 798, pp. 1-14. https://doi.org/10.3390/app7080798

APA

Iglesias, J., Ledezma, A., Sanchis, A., & Angelov, P. P. (2017). Real-time Recognition of Calling Pattern and Behaviour of Mobile Phone users through Anomaly Detection and Dynamically Evolving Clustering. Applied Sciences, 7(8), 1-14. Article 798. https://doi.org/10.3390/app7080798

Vancouver

Iglesias J, Ledezma A, Sanchis A, Angelov PP. Real-time Recognition of Calling Pattern and Behaviour of Mobile Phone users through Anomaly Detection and Dynamically Evolving Clustering. Applied Sciences. 2017 Aug 5;7(8):1-14. 798. doi: 10.3390/app7080798

Author

Iglesias, Jose ; Ledezma, Agapito ; Sanchis, Araceli et al. / Real-time Recognition of Calling Pattern and Behaviour of Mobile Phone users through Anomaly Detection and Dynamically Evolving Clustering. In: Applied Sciences. 2017 ; Vol. 7, No. 8. pp. 1-14.

Bibtex

@article{048348a19cb14848af81b11af041a6de,
title = "Real-time Recognition of Calling Pattern and Behaviour of Mobile Phone users through Anomaly Detection and Dynamically Evolving Clustering",
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.",
keywords = "Anomaly detection, clustering",
author = "Jose Iglesias and Agapito Ledezma and Araceli Sanchis and Angelov, {Plamen Parvanov}",
year = "2017",
month = aug,
day = "5",
doi = "10.3390/app7080798",
language = "English",
volume = "7",
pages = "1--14",
journal = "Applied Sciences",
issn = "2076-3417",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "8",

}

RIS

TY - JOUR

T1 - Real-time Recognition of Calling Pattern and Behaviour of Mobile Phone users through Anomaly Detection and Dynamically Evolving Clustering

AU - Iglesias, Jose

AU - Ledezma, Agapito

AU - Sanchis, Araceli

AU - Angelov, Plamen Parvanov

PY - 2017/8/5

Y1 - 2017/8/5

N2 - 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.

AB - 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.

KW - Anomaly detection

KW - clustering

U2 - 10.3390/app7080798

DO - 10.3390/app7080798

M3 - Journal article

VL - 7

SP - 1

EP - 14

JO - Applied Sciences

JF - Applied Sciences

SN - 2076-3417

IS - 8

M1 - 798

ER -