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

中图分类号: 

• TP311.12