Unsupervised User Similarity Mining in GSM Sensor Networks
Joint Authors
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-03-18
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Mobility data has attracted the researchers for the past few years because of its rich context and spatiotemporal nature, where this information can be used for potential applications like early warning system, route prediction, traffic management, advertisement, social networking, and community finding.
All the mentioned applications are based on mobility profile building and user trend analysis, where mobility profile building is done through significant places extraction, user’s actual movement prediction, and context awareness.
However, significant places extraction and user’s actual movement prediction for mobility profile building are a trivial task.
In this paper, we present the user similarity mining-based methodology through user mobility profile building by using the semantic tagging information provided by user and basic GSM network architecture properties based on unsupervised clustering approach.
As the mobility information is in low-level raw form, our proposed methodology successfully converts it to a high-level meaningful information by using the cell-Id location information rather than previously used location capturing methods like GPS, Infrared, and Wifi for profile mining and user similarity mining.
American Psychological Association (APA)
Shad, Shafqat Ali& Chen, Enhong. 2013. Unsupervised User Similarity Mining in GSM Sensor Networks. The Scientific World Journal،Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-1012557
Modern Language Association (MLA)
Shad, Shafqat Ali& Chen, Enhong. Unsupervised User Similarity Mining in GSM Sensor Networks. The Scientific World Journal No. 2013 (2013), pp.1-11.
https://search.emarefa.net/detail/BIM-1012557
American Medical Association (AMA)
Shad, Shafqat Ali& Chen, Enhong. Unsupervised User Similarity Mining in GSM Sensor Networks. The Scientific World Journal. 2013. Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-1012557
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1012557