计算机科学 ›› 2013, Vol. 40 ›› Issue (Z11): 251-254.

• 数据存储与挖掘 • 上一篇    下一篇

多粒度时间文本数据的周期模式挖掘算法

孟志青,楼婷渊,胡强   

  1. 浙江工业大学经贸管理学院 杭州310023;浙江工业大学经贸管理学院 杭州310023;浙江工业大学经贸管理学院 杭州310023
  • 出版日期:2018-11-16 发布日期:2018-11-16

Periodicity Algorithm of Textual Data Mining with Multi-granularity Time

MENG Zhi-qing,LOU Ting-yuan and HU Qiang   

  • Online:2018-11-16 Published:2018-11-16

摘要: 大规模文本数据挖掘是大数据分析的重要分支,也是近年来的一个研究热点。研究了多粒度时间文本数据周期模式挖掘算法,首先提出了时间粒度转换、多粒度时间间隔等概念,然后建立了文本数据的周期模型,给出了一个多粒度时间文本下的周期模式挖掘算法,最后对大量病毒文本文献数据进行了实验,表明了提出的算法可以挖掘一些有效的周期模式,讨论了周期宽松度对支持度和置信度的影响。该研究为大文本数据分析提供了一种新的方法。

关键词: 多粒度时间,文本数据,数据挖掘,周期模式

Abstract: The large-scale text data mining is an important branch of the big data analysis and is also a hot research to-pic in recent years.This paper studied algorithm of the textual periodicity data mining with multi-granularity time.First,the concepts of granularity conversion and multi-granularity time interval were presented.Then,a periodic pattern of textual data and an algorithm of the periodic pattern to textual data with multi-granularity time were proposed.Finally,by testing virus textual data,the proposed algorithm shows that some efficient periodic patterns are obtained.The influence of the periodic range on the degree of support and confidence were discussed.This paper provided a new method for the big text data analysis.

Key words: Multiple granularity,Textual data,Data mining,Periodic pattern

[1] Bettini C.Testing complex temporal relationships involving mul-tiple granularities and its application to data mining[J].ACM,1996,2(4):86-88
[2] Bettini C,Wang S X,Sushil J,et al.Discovering frequent event patternswithmultiple granularities in time sequences [J].IEEETransactions on Knowledge and Data Engineering,1998,0(2):222-237
[3] 孟志青.时态数据采掘中的时态型与时间粒度研究[J].湘潭大学自然科学学报,2000,2(3):1-4
[4] 孟志青.时态关联规则采掘的若干性质[J].计算机工程与应用,2001,7(10):42-44
[5] 姜华,孟志青,肖建华,等.一种时态近似周期的数据挖掘研究[J].软件技术与数据库,2006,2(22):61-63
[6] 程昱.时态数据周期挖掘理论与算法的研究[D].湘潭大学,2005
[7] Li Ying-jiu,Wang X,et al.Discovering Temporal Patterns inMultiple Granularitiesp [C]∥TSDM’ 00Proceeding of the First International Workshop on Temporal,Spatial,and Spatio-Temporal Data Mining-Revised Papers.2007:5-19

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!