期刊文献+

三种后缀单模式匹配算法的性能研究

Research on the Performance of Three Suffix Single Pattern Matching Algorithm
下载PDF
导出
摘要 分析Apostolico-Giancarlo(AG)算法、Reverse Colussi(RC)算法和Turbo Reverse Factor(TRF)算法的特点和时间空间复杂度.选取从不同的文本串和模式串,对三种算法进行消耗时间,尝试趟数两方面进行实验.实验结果表明,由于TRF算法采用自动机实现匹配,能大大地缩短匹配时间,因而能更有效地提高模式匹配速度. In this paper,the time and the space complexity of Apostolico-Giancarlo algorithm,Reverse Colussi algorithm and Turbo Reverse Factor algorithm are analyzed.Selecting the different text strings and pattern strings,it carries out the experiments from the time consuming and the numbers of attempts on the three algorithms.The experimental results show that the TRF algorithm can improve the pattern matching speed more effectively because it can shorten the matching time by using automaton to implement matching.
作者 巫喜红 WU Xi-hong(School of Computer,Jiaying University,Meizhou 514015,China)
出处 《嘉应学院学报》 2018年第5期8-12,共5页 Journal of Jiaying University
基金 2013年广东省科技计划项目(2013B040500010) 2016年广东省重点平台及科研项目(2016KTSCX129) 2016年嘉应学院自然科学重点项目(2016KJZ04)
关键词 后缀 单模式 AG算法 RC算法 TRF算法 suffix single pattern AG algorithm RC algorithm TRF algorithm
  • 相关文献

参考文献2

二级参考文献15

  • 1Hu Weiming,Li Xi,Tian Guodong,et al.An incremental dpmm-based method for trajectory clustering,modeling and retrieval[J].IEEE Trans on Pattern Analysis and Machine Intelligence,2013,35(5):1051-1065.
  • 2Chawla S,Gionis A,Chawla S,et al.K-means:a unified approach to clustering and outlier detection[C]//SIAM International Conference on Data Mining.2013.
  • 3Masciari E,Shi Gao,Zaniolo C.Sequential pattern mining from trajectory data[C]//Proc of the 17th International Database Engineering & Applications Symposium.[S.l.] :ACM Press,2013:162-167.
  • 4Zolotukhin M,Ivannikova E,Hamalainen T.Novel method for the prediction of mobile location based on temporal-spatial behavioral patterns[C]//International Conference on Information Science and Technology.[S.l.] :IEEE Press,2013:761-766.
  • 5Wang Jingwei,Yen N Y,Guo Bin,et al.User travelling pattern prediction via indistinct cellular data mining[C]//Proc of the 10th IEEE International Conference on Autonomic and Trusted Computing,Ubiquitous Intelligence and Computing.[S.l.] :IEEE Press,2013:17-24.
  • 6Yu Xinran,Korkmaz T.Super-sequence frequent pattern mining on sequential dataset[C]//IEEE International Conference on Big Data.[S.l.] :IEEE Press,2013:52-59.
  • 7Mathew W,Raposo R,Martins B.Predicting future locations with hidden Markov models[C]//Proc of ACM Conference on Ubiquitous Computing.[S.l.] :ACM Press,2012:911-918.
  • 8Asahara A,Maruyama K,Sato A,et al.Pedestrian-movement prediction based on mixed Markov-chain model[C]//Proc of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems.[S.l.] :ACM Press,2011:25-33.
  • 9Gambs S,Killijian M O,Del Prado Cortez M N.Next place prediction using mobility Markov chains[C]//Proc of the 1st Workshop on Measurement,Privacy,and Mobility.[S.l.] :ACM Press,2012:3.
  • 10Lothaire M.Applied combinatorics on words[M].[S.l.] :Cambridge University Press,2005.

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部