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Latent Clustering Models for Outlier Identification in Telecom Data
Joint Authors
Ouyang, Ye
Huet, Alexis
Shim, J. P.
Hu, Mantian (Mandy)
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-12-14
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Telecommunications Engineering
Abstract EN
Collected telecom data traffic has boomed in recent years, due to the development of 4G mobile devices and other similar high-speed machines.
The ability to quickly identify unexpected traffic data in this stream is critical for mobile carriers, as it can be caused by either fraudulent intrusion or technical problems.
Clustering models can help to identify issues by showing patterns in network data, which can quickly catch anomalies and highlight previously unseen outliers.
In this article, we develop and compare clustering models for telecom data, focusing on those that include time-stamp information management.
Two main models are introduced, solved in detail, and analyzed: Gaussian Probabilistic Latent Semantic Analysis (GPLSA) and time-dependent Gaussian Mixture Models (time-GMM).
These models are then compared with other different clustering models, such as Gaussian model and GMM (which do not contain time-stamp information).
We perform computation on both sample and telecom traffic data to show that the efficiency and robustness of GPLSA make it the superior method to detect outliers and provide results automatically with low tuning parameters or expertise requirement.
American Psychological Association (APA)
Ouyang, Ye& Huet, Alexis& Shim, J. P.& Hu, Mantian (Mandy). 2016. Latent Clustering Models for Outlier Identification in Telecom Data. Mobile Information Systems،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1111364
Modern Language Association (MLA)
Ouyang, Ye…[et al.]. Latent Clustering Models for Outlier Identification in Telecom Data. Mobile Information Systems No. 2016 (2016), pp.1-11.
https://search.emarefa.net/detail/BIM-1111364
American Medical Association (AMA)
Ouyang, Ye& Huet, Alexis& Shim, J. P.& Hu, Mantian (Mandy). Latent Clustering Models for Outlier Identification in Telecom Data. Mobile Information Systems. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1111364
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1111364