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计算机工程

• 先进计算与数据处理 • 上一篇    下一篇

基于中国观鸟数据的移动对象周期模式发现

陈东1,2,邵增珍1,2,魏争争1,2,刘衍民3   

  1. (1.山东师范大学 信息科学与工程学院,济南 250014; 2.山东省物流优化与预测工程技术研究中心,济南 250014;3.遵义师范学院 数学与计算科学学院,贵州 遵义 563002)
  • 收稿日期:2016-05-17 出版日期:2017-04-15 发布日期:2017-04-14
  • 作者简介:陈东(1990—),男,硕士,主研方向为数据挖掘;邵增珍,副教授、博士、博士后;魏争争,硕士;刘衍民,教授、博士。
  • 基金资助:
    中国博士后科学基金(2016M592697);山东省科技发展计划项目(2014GGH201022);山东省经信委软科学研究课题(2015 EI010)。

Periodic Pattern Discovery of Moving Objects Based on China Birding Data CHEN Dong1,2,SHAO Zengzhen1,2,WEI Zhengzheng1,2,LIU Yanmin3

CHEN Dong  1,2,SHAO Zengzhen  1,2,WEI Zhengzheng  1,2,LIU Yanmin  3   

  1. (1.School of Information Science and Engineering,Shandong Normal University,Jinan 250014,China;2.Shandong Provincial Logistics Optimization and Predictive Engineering Technology Research Center,Jinan 250014,China; 3.School of Mathematics and Computing Science,Zunyi Normal College,Zunyi,Guizhou 563002,China)
  • Received:2016-05-17 Online:2017-04-15 Published:2017-04-14

摘要: 移动对象的轨迹数据中包含大量时空信息,挖掘时空信息背后隐藏的周期模式对掌握移动对象变化规律具有重要作用。为此,提出一种三阶段移动对象周期模式检测算法,通过研究轨迹点的时空特征识别并剔除重复数据,利用密度聚类算法发现轨迹点密集区域并找出密集区域中每一类移动对象的周期模式,解决移动对象轨迹周期模式挖掘中轨迹数据重复、采样数据不连续及潜在周期模式发现问题。基于2003年—2015年中国观鸟记录中心、中国观鸟年报等公开数据的实验结果表明,该算法可有效处理轨迹数据并准确挖掘出规律性移动对象的周期模式。

关键词: 移动对象, 数据挖掘, 数据预处理, 周期模式, 中国观鸟数据

Abstract: The trajectory data of moving objects contains a large amount of spatio-temporal information,and mining the periodic pattern hidden behind the spatio-temporal information is of great significance.In this paper,an algorithm for detecting the periodic pattern of the moving objects based on three stages is proposed.Through the study of the temporal and spatial characteristics of the trajectory points,it identifies and eliminates duplicate data.Density clustering algorithm is used to find the dense region of the locus and the periodic pattern of each moving object in the dense region,which solves the problem of the repetition of the trajectory data,the incontinuity of sampling data and the finding of the periodic pattern period of the moving objects.Experimental results based on 2003—2015 China birding record center,China Birding Report(CBR) and other public data show that this algoithm can process the trajectory data effectively and dig out the periodic pattern of the moving objects with regularity accurately.

Key words: moving object, data mining, data preprocessing, periodic pattern, China birding data

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