Clustering Indoor Positioning Data Using E-DBSCAN
文献类型：期刊论文
| 作者 | Cheng, Dayu2,3; Yue, Guo1; Pei, Tao2; Wu, Mingbo2,4 |
| 刊名 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
 |
| 出版日期 | 2021-10-01 |
| 卷号 | 10期号:10页码:25 |
| 关键词 | indoor positioning data spatial-temporal mobility weighted edit distance E-DBSCAN trajectory clustering |
| DOI | 10.3390/ijgi10100669 |
| 通讯作者 | Pei, Tao(peit@lreis.ac.cn) |
| 英文摘要 | Indoor positioning data reflects human mobility in indoor spaces. Revealing patterns of indoor trajectories may help us understand human indoor mobility. Clustering methods, which are based on the measurement of similarity between trajectories, are important tools for identifying those patterns. However, due to the specific characteristics of indoor trajectory data, it is difficult for clustering methods to measure the similarity between trajectories. These characteristics are manifested in two aspects. The first is that the nodes of trajectories may have clear semantic attributes; for example, in a shopping mall, the node of a trajectory may contain information such as the store type and visit duration time, which may imply a customer's interest in certain brands. The semantic information can only be obtained when the position precision is sufficiently high so that the relationship between the customer and the store can be determined, which is difficult to realize for outdoor positioning, either using GPS or mobile base station, due to the relatively large positioning error. If the tendencies of customers are to be considered, the similarity of geometrical morphology does not reflect the real similarity between trajectories. The second characteristic is the complex spatial shapes of indoor trajectory caused by indoor environments, which include elements such as closed spaces, multiple obstacles and longitudinal extensions. To deal with these challenges caused by indoor trajectories, in this article we proposed a new method called E-DBSCAN, which extended DBSCAN to trajectory clustering of indoor positioning data. First, the indoor location data were transformed into a sequence of residence points with rich semantic information, such as the type of store customer visited, stay time and spatial location of store. Second, a Weighted Edit Distance algorithm was proposed to measure the similarity of the trajectories. Then, an experiment was conducted to verify the correctness of E-DBSCAN using five days of positioning data in a shopping mall, and five shopping behavior patterns were identified and potential explanations were proposed. In addition, a comparison was conducted among E-DBSCAN, the k-means and DBSCAN algorithms. The experimental results showed that the proposed method can discover customers' behavioral pattern in indoor environments effectively. |
| WOS关键词 | SIMILARITY ; ALGORITHM ; FRAMEWORK |
| 资助项目 | National Key Research and Development Program of China[2017YFB0503602] |
| WOS研究方向 | Computer Science ; Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000711641600001 |
| 出版者 | MDPI |
| 资助机构 | National Key Research and Development Program of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/167520]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Pei, Tao |
| 作者单位 | 1.China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou 221116, Jiangsu, Peoples R China 2.Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Hebei Univ Engn, Sch Min & Geomat Engn, Handan 056038, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cheng, Dayu,Yue, Guo,Pei, Tao,et al. Clustering Indoor Positioning Data Using E-DBSCAN[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2021,10(10):25. |
| APA | Cheng, Dayu,Yue, Guo,Pei, Tao,&Wu, Mingbo.(2021).Clustering Indoor Positioning Data Using E-DBSCAN.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,10(10),25. |
| MLA | Cheng, Dayu,et al."Clustering Indoor Positioning Data Using E-DBSCAN".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 10.10(2021):25. |
入库方式： OAI收割
来源：地理科学与资源研究所
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