《计算机应用研究》|Application Research of Computers

基于时空因素聚类算法的研究与应用

Research and application of clustering algorithm based on factor of time and space

免费全文下载 (已被下载 次)  
获取PDF全文
作者 闫双舰,罗泽,阎保平
机构 1.中国科学院计算机网络信息中心e-Science应用推进总体组,北京 100190;2.中国科学院大学 信息学院,北京 100049
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2013)12-3690-04
DOI 10.3969/j.issn.1001-3695.2013.12.044
摘要 为了发现移动对象的迁徙轨迹和经停地, 提出结合经停地检测算法和单链接聚类算法的方法。通过青海湖鸟类的历史位置信息验证该方法的准确性和有效性, 并与应用于本领域的其他方法进行分析比较, 如DBSCAN聚类算法、减聚类及模糊聚类算法。结果显示提出的方法能够克服对比算法仅考虑迁徙数据空间位置信息的缺点, 准确有效地挖掘出鸟类经停地和迁徙轨迹。
关键词 聚类分析;经停地检测算法;单链接算法;迁徙轨迹;经停地发现
基金项目
本文URL http://www.arocmag.com/article/01-2013-12-044.html
英文标题 Research and application of clustering algorithm based on factor of time and space
作者英文名 YAN Shuang-jian, LUO Ze, YAN Bao-ping
机构英文名 1. e-Science application advance Zongtizu, Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China; 2. Information School, University of Chinese Academy of Sciences, Beijing 100049, China
英文摘要 In order to discover the migratory trajectory and stopover, this paper proposed a method combining stopover detection algorithm and single-linkage cluster algorithm. It verified the accuracy and effectiveness of the above method by the history location information of the birds in Qinghai Lake, and other methods applied to the field was analyzed and compared, such as DBSCAN clustering algorithm, subtractive clustering and fuzzy clustering algorithm. The result shows that the proposed method can overcome the shortcomings of conventional approaches that only consider the space location information of the migratory data, and discover the bird stopover and migratory trajectory accurately and effectively.
英文关键词 clustering analysis; stopover detection algorithm; single linkage algorithm; migratroy trajectory; stopover disco-very
参考文献 查看稿件参考文献
  [1] TANG Ming-jie, ZHOU Yuan-chun. Discovery of migration habitats and routes of wild bird species by clustering and association analysis[C] //Proc of the 5th International Conference on Advanced Data Mining and Application. [S. l. ] :Springer-Verlag, 2009:288-301.
[2] CUI Peng, HOU Yuan-sheng. Bird migration and risk for H5N1 transmission into Qinghai lake[J] . China:Vector-Borne and Zoonotic Diseases, 2010, 11(5):567-576.
[3] TANG Ming-jie, ZHOU Yuan-chun, LI Jin-yan. Exploring the wild birds migration data for the disease spread study of H5N1:a clustering and association approach[J] . Knowledge and Information Systems, 2011, 27(2):227-251.
[4] LI Sha-sha, ZHOU Yuan-chun, KOU Zheng. A wavelet packet based approach for the research of the Avian influenza virus cross-species infection[C] //Proc of the 1st International Conference on Networking and Distributed Computing. 2010:301-304.
[5] CARNEIRO C, ALP A, MACEDO J. Advanced data mining method for discovering regions and trajectories of moving objects:“Ciconia Ciconia” scenario[C] //Lecture Notesin Geoinformation and Cartography. Berlin:Springer-Verlag, 2008:201-224.
[6] PRIYONO A, RIDWAN M, ALIAS J A, et al. Generation of fuzzy rules with subtractive clustering[J] . Journal Teknologi, 2012, 43(D):143-153.
[7] ZHENG Yu, XIE Xing. Learning location correlation from GPS trajectories[C] //Proc of the 11th International Conference on Mobile Data Management. 2010:27-32.
[8] LI Quan-nan, ZHENG Yu, XIE Xing, et al. Mining user similarity based on location history [C] // Proc of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York:ACM Press, 2008:1-10.
[9] ZHENG Yu, ZHANG Li-zhu, MA Zheng-xin. Recommending friends and locations based on individual location history[J] . ACM Tran-saction on the Web, 2011, 5(1):1-44.
[10] HAN Jia-wei, KAMBER M, PEI Jian. Data mining concepts and techniques[M] . 3rd ed. [S. L. ] :Morgan Kaufmann, 2011.
[11] WARD S, BISHOP C M, WOAKES A J, et al. Heart rate and the rate of oxygen consumption of flying and walking barnacle geese and bar-headed geese [J] . Journal of Experimental Biology, 2002, 205(21):3347-3356.
[12] GIANNOTTI F, NANNI M, PINELLI F, et al. Trajectory pattern mi-ning[C] //Proc of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM Press, 2007:330-339.
[13] VERHEIN F, CHAWLA S. Mining spatio-temporal patterns in object mobility databases[J] . Data Mining and Knoledge Discovery, 2008, 16(1):5-38.
[14] LI Xiao-lei, HAN Jia-wei, KIM S, et al. ROAM:rule-and motif-based anomaly detection in massive moving object data sets[C] //Proc of the 7th SIAM International Conference on Data Mining. 2007.
收稿日期
修回日期
页码 3690-3693,3697
中图分类号 TP311.1
文献标志码 A