计算机科学 ›› 2019, Vol. 46 ›› Issue (11): 251-259.doi: 10.11896/jsjkx.191100505C
王一博1,2, 彭广举1,2, 何远舵1,2, 王亚沙1,3, 赵俊峰1,2, 王江涛1,2
WANG Yi-bo1,2, PENG Guang-ju1,2, HE Yuan-duo1,2, WANG Ya-sha1,3, ZHAO Jun-feng1,2, WANG Jiang-tao1,2
摘要: 随着传感器的普及,智慧城市、普适计算等领域应用不断涌现,对时序数据处理的需求也在不断增长。时序数据中反复出现的高度相似的模式被称为主题模式。时序数据的主题模式蕴含有了大量的信息,对主题模式的识别是时序数据处理的重要分支领域。现有主题模式识别算法无法根据特定应用或领域的知识来指定主题模式识别的偏好,从而难以发现对分析领域问题最具价值的模式。针对这一问题,文中给出了一种可以根据领域偏好定义子序列相似性的机制,并设计了一种针对上述相似性度量机制的可变时间窗口主题模式识别加速剪枝算法。实验证明,所提方法在多个公开数据集上,能高效且准确地发现具有领域偏好的主题模式。
中图分类号:
[1]BOX G E P,JENKINS G M,REINSEL G C,et al.Time series analysis:forecasting and control[M].New Jersey:John Wiley & Sons,2015. [2]PATEL P,KEOGH E,LIN J,et al.Mining motifs in massive time series databases[C]∥Proceedings of 2002 IEEE International Conference on Data Mining.IEEE,2002:370-377. [3]LONARDI J,PATEL P.Finding motifs in time series[C]∥Proceedings of the 2nd Workshop on Temporal Data Mining.2002:53-68. [4]WANG H,ZHANG D,WANG Y,et al.RT-Fall:A real-time and contactless fall detection system with commodity WiFi devices[J].IEEE Transactions on Mobile Computing,2017,16(2):511-526. [5]BROWN A E X,YEMINI E I,GRUNDY L J,et al.A dictionary of behavioral motifs reveals clusters of genes affecting Caenorhabditis elegans locomotion[J].Proceedings of the National Academy of Sciences,2013,110(2):791-796. [6]LIN J,KEOGH E,FU A,et al.Approximations to magic:Finding unusual medical time series[C]∥18th IEEE Symposium on Computer-Based Medical Systems (CBMS’05).IEEE,2005:329-334. [7]BARRENETXEA G,INGELREST F,SCHAEFER G,et al.Sensorscope:Out-of-the-box environmental monitoring[C]∥Proceedings of the 7th International Conference on Information Processing in Sensor Networks.IEEE Computer Society,2008:332-343. [8]MCGOVERN A,ROSENDAHL D H,BROWN R A,et al.Identifying predictive multi-dimensional time series motifs:an application to severe weather prediction[J].Data Mining and Know-ledge Discovery,2011,22(1-2):232-258. [9]SHOKOOHI-YEKTA M,CHEN Y,CAMPANA B,et al.Discovery of meaningful rules in time series[C]∥Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2015:1085-1094. [10]KEOGH E,KASETTY S.On the need for time series datamining benchmarks:a survey and empirical demonstration[J].Data Mining and Knowledge Discovery,2003,7(4):349-371. [11]MUEEN A,KEOGH E,ZHU Q,et al.Exact discovery of time series motifs[C]∥Proceedings of the 2009 SIAM International Conference on Data Mining.Society for Industrial and Applied Mathematics,2009:473-484. [12]YEH C C M,ZHU Y,ULANOVA L,et al.Matrix profile I:all pairs similarity joins for time series:a unifying view that includes motifs,discords and shapelets[C]∥2016 IEEE 16th international conference on data mining (ICDM).IEEE,2016:1317-1322. [13]ZHU Y,ZIMMERMAN Z,SENOBARI N S,et al.Matrix profile ii:Exploiting a novel algorithm and gpus to break the one hundred million barrier for time series motifs and joins[C]∥2016 IEEE 16th International Conference on Data Mining (ICDM).IEEE,2016:739-748. [14]MUEEN A,CHAVOSHI N.Enumeration of time series motifs of all lengths[J].Knowledge and Information Systems,2015,45(1):105-132. [15]DAU H A,KEOGH E.Matrix Profile V:A Generic Technique to Incorporate Domain Knowledge into Motif Discovery[C]∥Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2017:125-134. [16]MAKONIN S,ELLERT B,BAJIC' I V,et al.Electricity,water,and natural gas consumption of a residential house in Canada from 2012 to 2014[J].Scientific Ata,2016,3:160037-160037. [17]KUBÁNEK J,MILLER K J,OJEMANN J G,et al.DecodingFlexion Of Individual Fingers Using electrocorticographic signals in humans[J].Journal of Neural Engineering,2009,6(6):066001-066001. |
[1] | 程文聪,邹鹏,贾焰. 基于小波概要的区间差分skyline研究 Research on Interval Differential Skyline Based on Wavelet Synopsis 计算机科学, 2010, 37(11): 160-165. |
[2] | 宋应湃 汪林林. 数据挖掘技术在IT基础设施监控系统中的应用 计算机科学, 2007, 34(5): 205-207. |
[3] | 肖晶 黄国兴 赵若韵 黄豫蕾. 时间序列的快速相似性搜索改进算法 计算机科学, 2003, 30(9): 97-99. |
[4] | 周丽华 王丽珍. 基于小波变换的例外挖掘 计算机科学, 2002, 29(2): 127-129. |
[5] | 段立娟 高文. 时序数据库中相似序列的挖掘 计算机科学, 2000, 27(5): 39-44. |
[6] | 王清毅 范焱. 基于时序逻辑的时序数据库中知识发现方法 计算机科学, 1999, 26(8): 68-70. |
|