The discovery of personally semantic places based on trajectory data mining

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• 作者：Mingqi Lv^a ; ^b ; ^{mingqilv@zjut.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Ling Chen^b ; ^{lingchen@cs.zju.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Zhenxing Xu^b ; ^{xuzhenxing2010@163.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Yinglong Li^a ; ^{liyinglong@zjut.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Gencai Chen^b ; ^{chengc@zju.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Trajectory data mining ; Place extraction ; Place recognition ; Location-aware computing
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：15 January 2016
• 年：2016
• 卷：173
• 期：part_P3
• 页码：1142-1153
• 全文大小：1279 K

文摘

A personally semantic place is a space that is frequently visited by an individual user and carries important semantic meanings (e.g. home, work, etc.) to the user. Many location-aware applications could be greatly enhanced by the ability of automatic discovery of personally semantic places. The discovery of a user's personally semantic places involves obtaining the physical locations and semantic meanings of these places. In this paper, we propose approaches to address both of the problems. For the physical place extraction problem, a hierarchical clustering algorithm is proposed to firstly extract visit points from the GPS trajectories, and then clusters these visit points to form physical places. For the semantic place recognition problem, the temporal, spatial and sequential features in which the places have been visited are explored to categorize them into pre-defined types. An extensive set of experiments conducted based on a dataset of real-world GPS trajectories has demonstrated the effectiveness of the proposed approaches.