Unsupervised User Similarity Mining in GSM Sensor Networks

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 fin...

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Main Authors: Shafqat Ali Shad, Enhong Chen
Format: Article
Language:English
Published: Wiley 2013-01-01
Series:The Scientific World Journal
Online Access:http://dx.doi.org/10.1155/2013/589610
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author Shafqat Ali Shad
Enhong Chen
author_facet Shafqat Ali Shad
Enhong Chen
author_sort Shafqat Ali Shad
collection DOAJ
description 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.
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institution Kabale University
issn 1537-744X
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publishDate 2013-01-01
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spelling doaj-art-bbcef07c46354b18bacb9b55b0d8544d2025-02-03T01:22:23ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/589610589610Unsupervised User Similarity Mining in GSM Sensor NetworksShafqat Ali Shad0Enhong Chen1Department of Computer Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, Anhui 230027, ChinaDepartment of Computer Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, Anhui 230027, ChinaMobility 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.http://dx.doi.org/10.1155/2013/589610
spellingShingle Shafqat Ali Shad
Enhong Chen
Unsupervised User Similarity Mining in GSM Sensor Networks
The Scientific World Journal
title Unsupervised User Similarity Mining in GSM Sensor Networks
title_full Unsupervised User Similarity Mining in GSM Sensor Networks
title_fullStr Unsupervised User Similarity Mining in GSM Sensor Networks
title_full_unstemmed Unsupervised User Similarity Mining in GSM Sensor Networks
title_short Unsupervised User Similarity Mining in GSM Sensor Networks
title_sort unsupervised user similarity mining in gsm sensor networks
url http://dx.doi.org/10.1155/2013/589610
work_keys_str_mv AT shafqatalishad unsupervisedusersimilarityminingingsmsensornetworks
AT enhongchen unsupervisedusersimilarityminingingsmsensornetworks