计算机工程 ›› 2010, Vol. 36 ›› Issue (3): 83-85,8.doi: 10.3969/j.issn.1000-3428.2010.03.027

• 软件技术与数据库 • 上一篇    下一篇

多粒度时间下的近似周期挖掘研究

姜 华1,孟志青2,周克江1,肖建华1   

  1. (1. 湖南第一师范学院信息科学与工程系,长沙 410205;2. 浙江工业大学经贸管理学院,杭州 310023)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2010-02-05 发布日期:2010-02-05

Study of Approximate Periodicity Mining with Multi-granularity Time

JIANG Hua1, MENG Zhi-qing2, ZHOU Ke-jiang1, XIAO Jian-hua1   

  1. (1. Dept. of Information Science and Engineering, Hunan First Normal University, Changsha 410205; 2. College of Business and Administration, Zhejiang University of Technology, Hangzhou 310023)
  • Received:1900-01-01 Revised:1900-01-01 Online:2010-02-05 Published:2010-02-05

摘要: 研究时态数据库中多粒度时间下的近似周期的挖掘问题。在多粒度时间、多粒度时间格式的基础上引入多粒度时间间隔的定义以及相关性质,构造多粒度近似周期模型,提出一个基于SOM聚类的多粒度近似周期的挖掘算法。利用高频股票数据580000宝钢JBT1进行实验,证明了该算法的有效性。

关键词: 数据挖掘, 自组织映射网络, 多粒度时间, 近似周期

Abstract: This paper discusses a mining problem of approximate periodicity with multi-granularity time in the temporal database. It introduces the concepts and properties of the multi-granularity time interval on the basis of multi-granularity time and multi-granularity time format. It constructs multi-granularity approximate periodic pattern. It proposes an mining algorithm based on self-organizing map to find multi-granularity approximate periodic pattern. Results obtained from experiments on high frequency stock market data of 580000 Bao Steel JBT1 demonstrate that the proposed algorithm is efficient.

Key words: data mining, Self-Organizing Map(SOM) network, multi-granularity time, approximate periodicity

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