Unsupervised inference of significant locations from WiFi data for understanding human dynamics

Thanh-Binh Nguyen, Thuong Nguyen, Wei Luo, Svetha Venkatesh, Dinh Phung

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

6 Citations (Scopus)


Motion and location activities are essential to understanding human dynamics. This paper presents a method for discovering significant locations and individuals' daily routines from WiFi data, a data source considered more suitable for analyzing human dynamics than GPS data. Our method determines significant locations by clustering access points in close proximity using the Affinity Propagation algorithm. We demonstrate the method on the MDC dataset that includes more than 30 million WiFi scans. The experimental results show a high clustering performance for most of the users. The discovered location trajectories revealed interesting mobility patterns of mobile phone users. The human dynamics of participants is reflected through the entropy of the location distributions which shows interesting correlation with the age and occupations of users. Quantitative results are presented to support our proposed approach.

Original languageEnglish
Title of host publicationProceedings of the The 13th International Conference on Mobile and Ubiquitous Multimedia (MUM2014)
Subtitle of host publication25-27 November, Melbourne, Australia
EditorsSeng W. Loke, Arkady Zaslavsky, Lars Kulik, Evaggelia Pitoura
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages4
ISBN (Electronic)9781450329453, 9781450331852, 9781450333047
Publication statusPublished - 2014
Externally publishedYes
EventMobile and Ubiquitous Multimedia (ACM) 2014 - Melbourne, Australia
Duration: 25 Nov 201427 Nov 2014
Conference number: 13th


ConferenceMobile and Ubiquitous Multimedia (ACM) 2014
Abbreviated titleMUM 2014
Internet address

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