随机采样移动轨迹时空热点区域发现及模式挖掘
Discovering spatiotemporal hot spot region and mining patterns from moving trajectory random sampling
查看参考文献15篇
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文摘
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针对随机采样条件下移动轨迹在时间轴分布疏密不均的特点,在将三维时空轨迹转换为一维时间投影数据的基础上,提出一种基于密集时间区间自动检测的时空热点区域发现与移动模式挖掘方法。通过自底向上的动态聚类方式以探测密集时间区间,进而在密集时间区间内进行移动轨迹的时空热点区域发现。最后,采用深度优先的序列模式挖掘算法挖掘频繁移动模式集合。基于合成数据的仿真试验,验证了算法在有效性及可扩展性方面均具有较好的性能。 |
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其他语种文摘
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The moving trajectory by random sampling distributes unevenly in time dimension. After projecting the three-dimensional spatiotemporal trajectory data into one-dimensional time domain,a spatiotemporal hot spot region discovery and moving pattern mining methods are proposed based on automatic detection of intensive time intervals. Through detecting intensive time intervals dynamically with a bottom-up clustering strategy,the spatiotemporal hot spot regions are discovered in corresponding time intervals. A depth-first algorithm is designed to mine the set of frequency moving patterns. Finally,based on synthetic moving trajectory dataset,the effectiveness and scalability of the proposed algorithms are verified. |
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来源
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吉林大学学报. 工学版
,2015,45(3):913-920 【核心库】
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DOI
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10.13229/j.cnki.jdxbgxb201503033
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关键词
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人工智能
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数据挖掘
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随机采样移动轨迹
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密集时间区间
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热点区域
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地址
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中国科学院沈阳自动化研究所, 沈阳, 110016
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1671-5497 |
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学科
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自动化技术、计算机技术 |
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基金
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国家自然科学基金项目
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文献收藏号
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CSCD:5431154
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参考文献 共
15
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