基于位置数据的用户多周期移动行为挖掘
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摘要
挖掘位置数据中的用户行为规律是大数据时代的研究热点之一.现有研究主要关注于用户在某时刻出现在某地点的行为,对于用户从一个地点移动到另一个地点的动态行为研究较为空缺.提出一种挖掘位置数据中用户移动行为的算法可以发现用户的多个周期移动行为,描述用户在时空上的移动规律.首先,利用离散傅里叶变换和自相关系数检测用户移动行为的周期,在这一过程中,利用Apriori性质减少计算复杂度;而后提出用户移动行为的生成模型,估计用户的移动行为概率矩阵,考虑到观测数据的稀疏性,采用带全局限制的动态时间规整距离对不同时间段的行为进行聚类以发现用户的多个周期移动行为.最后,我们选取某市公共自行车系统收集的位置数据进行实证分析,结果表明,新方法能有效地挖掘用户的多个周期移动行为,进一步地,通过归纳可以得到用户群体在周期移动行为上的主要特征.
Mining the rule of users' behaviors in location data is one of the research hotspots in the era of big data.Existing researches focus on the behavior of users appearing at a certain place at a certain moment,and the dynamic behavior research of users moving from one place to another is relatively empty.Proposing an algorithm to mine user's moving behavior based on location data can discover users' multiple periodic moving behaviors and describe users' moving rules in time and space.Firstly,we use Discrete Fourier Transform and Autocorrelation Coefficient to detect the period of users' moving behaviors.In this process,Apriori property is considered to reduce the computational complexity.Then,we propose a generative model and estimate the probability matrix of users' moving behaviors.Taking into account the sparsity of users' observation data,we cluster the behaviors of different time periods based on the dynamic time warping distance with global constraints to find the users' multiple periodic moving behaviors.Finally,we select the location data collected by the public bicycle system in some city for empirical analysis.The results show that the new method can effectively mine users' multiple periodic moving behaviors.Moreover,we summarize the main characteristics of the periodic moving behaviors for the users group.
Mining the rule of users' behaviors in location data is one of the research hotspots in the era of big data.Existing researches focus on the behavior of users appearing at a certain place at a certain moment,and the dynamic behavior research of users moving from one place to another is relatively empty.Proposing an algorithm to mine user's moving behavior based on location data can discover users' multiple periodic moving behaviors and describe users' moving rules in time and space.Firstly,we use Discrete Fourier Transform and Autocorrelation Coefficient to detect the period of users' moving behaviors.In this process,Apriori property is considered to reduce the computational complexity.Then,we propose a generative model and estimate the probability matrix of users' moving behaviors.Taking into account the sparsity of users' observation data,we cluster the behaviors of different time periods based on the dynamic time warping distance with global constraints to find the users' multiple periodic moving behaviors.Finally,we select the location data collected by the public bicycle system in some city for empirical analysis.The results show that the new method can effectively mine users' multiple periodic moving behaviors.Moreover,we summarize the main characteristics of the periodic moving behaviors for the users group.
引文
[1]陈康,黄晓宇,王爱宝,陶彩霞,关迎晖,李磊.基于位置信息的用户行为轨迹分析与应用综述[J].电信科学,2013,4:118-123.
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[11]Berberidis C,Vlahavas I P,Aref W G,Atallah M J,and Elmagarmid A K.On the discovery of weak periodicities in large time series[C]//In Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery,2002:51-61.
[12]Muller M.Information retrieval for music and motion[M].Springer Verlag,2007.
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[14]Vlachos M,Yu P,and Castelli V.On periodicity detection and structural periodic similaxity[C]//In Proceedings of the 2005 SIAM International Conference on Data Mining,2005:449-460.
[15]Kaufman L,and Rousseeuw PJ.Finding groups in data:an introduction to cluster analysis[M].Wiley,1990.
[16]Tibshirani R,Walther G,and Hastie T.Estimating the number of clusters in a data set via the gap statistic[J].Journal of the Royal Statistical Society B.2001,63(2):411-423.
[2]郭迟,刘经南,方媛,罗梦,崔竞松.位置大数据的价值提取与协同挖掘方法[J].软件学报,2014,4:713-730.
[3]Jindal T,Giridhar P,Tang L A,Li J,and Han J.Spatiotemporal periodical pattern mining in traffic data[C].In Proceedings of the 2Nd ACM SIGKDD International Workshop on Urban Computing,2013:1-8.
[4]Tang Y,Lin C,Yuan Y,and Deng D J.Dividing sensitive ranges based mobility prediction algorithm in wireless networks[J].Tamkang Journal of Science and Engineering,2010,13(1):107-115.
[5]Daoud M S,Ayesh A,Al-Fayoumi M,and Hopgood A A.Location prediction based on a sector snapshot for location-based services[J].Journal of Network and Systems Management,2014,22(1):23-49.
[6]Balamurugan V.Mining user mobile behavior in location based services[J].International Journal of Scientific and Research Publications,2012,2(9):1-3.
[7]Liu H,and Schneider M.Similarity measurement of moving object trajectories[C]//In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoStreaming,2012:19-22.
[8]Song C,Qu Z,Blumm N,and Barabasi A.Limits of predictability in human mobility[J].Science,2010,327(5968):1018-1021.
[9]Kirmse A,Udeshi T,Bellver P,and Shuma J.Extracting patterns from location history[C].In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems,2011:397-400.
[10]Li Z,Ding B,Han J,Kays R,and Nye P.Mining periodic behaviors for moving objects[C]//In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2010:1099-1108.
[11]Berberidis C,Vlahavas I P,Aref W G,Atallah M J,and Elmagarmid A K.On the discovery of weak periodicities in large time series[C]//In Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery,2002:51-61.
[12]Muller M.Information retrieval for music and motion[M].Springer Verlag,2007.
[13]潘纲,李石坚,齐观德,张王晟.移动轨迹数据分析与智慧城市.中国计算机学会通讯[J].2012,8(5):31-37.
[14]Vlachos M,Yu P,and Castelli V.On periodicity detection and structural periodic similaxity[C]//In Proceedings of the 2005 SIAM International Conference on Data Mining,2005:449-460.
[15]Kaufman L,and Rousseeuw PJ.Finding groups in data:an introduction to cluster analysis[M].Wiley,1990.
[16]Tibshirani R,Walther G,and Hastie T.Estimating the number of clusters in a data set via the gap statistic[J].Journal of the Royal Statistical Society B.2001,63(2):411-423.