Association Rule Mining Method Based on the Similarity Metric of Tuple-Relation in Indoor Environment
文献类型：期刊论文
| 作者 | Mou, Naixia2,3; Wang, Hongen2,3; Zhang, Hengcai3; Fu, Xin1 |
| 刊名 | IEEE ACCESS
 |
| 出版日期 | 2020 |
| 卷号 | 8页码:52041-52051 |
| 关键词 | Association rule data mining indoor trajectory network embedding |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2020.2980952 |
| 通讯作者 | Mou, Naixia(mounaixia@163.com) ; Fu, Xin(stu_fux@126.com) |
| 英文摘要 | Association rules can detect the association pattern between POIs (point of interest) and serve the application of indoor location. In this paper, a new index, tuple-relation, is defined, which reflects the association strength between POI sets in indoor environment. This index considers the potential association information such as spatial and semantic information between indoor POI sets. On this basis, a new R-FP-growth (tuple-relation frequent pattern growth) algorithm for mining association rules in indoor environment is proposed, which makes comprehensive use of the co-occurrence probability, conditional probability, and multiple potential association information among POI sets, to form a new support-confidence-relation constraint framework and to improve the quality and application value of mining results. Experiments are performed, using real Wi-Fi positioning trajectory data from a shopping mall. Experimental results show that the tuple-relation calculation method based on cosine similarity has the best effect, with an accuracy of 87 & x0025;, and 19 & x0025; higher than that of the traditional FP-growth algorithm. |
| WOS关键词 | FREQUENT |
| 资助项目 | National Key Research and Development Program of China[2016YFB0502104] ; National Natural Science Foundation of China[41701521] ; National Natural Science Foundation of China[41771476] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000524748500075 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/134036]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Mou, Naixia; Fu, Xin |
| 作者单位 | 1.Univ Jinan, Sch Water Conservancy & Environm, Jinan 250022, Peoples R China 2.Shandong Univ Sci & Technol, Coll Geomat, Qingdao 266590, Peoples R China 3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, IGSNRR, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Mou, Naixia,Wang, Hongen,Zhang, Hengcai,et al. Association Rule Mining Method Based on the Similarity Metric of Tuple-Relation in Indoor Environment[J]. IEEE ACCESS,2020,8:52041-52051. |
| APA | Mou, Naixia,Wang, Hongen,Zhang, Hengcai,&Fu, Xin.(2020).Association Rule Mining Method Based on the Similarity Metric of Tuple-Relation in Indoor Environment.IEEE ACCESS,8,52041-52051. |
| MLA | Mou, Naixia,et al."Association Rule Mining Method Based on the Similarity Metric of Tuple-Relation in Indoor Environment".IEEE ACCESS 8(2020):52041-52051. |
入库方式： OAI收割
来源：地理科学与资源研究所
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