摘要
为了高效地执行多变量时间序列(MTS)相似查询,提出一种基于距离的索引结构(Dbis)相似查询算法。采用主成分分析方法对MTS数据进行降维处理;聚类MTS主成分序列,选择每类质心作为参考点;依据参考点将每类变换到一维空间,这样可以利用B+-树结构进行索引查询;MTS序列比较相似采用的是扩展的Frobenius范数(Eros)。通过对股票数据集实验验证了Dbis算法的高效性。
In order to efficiently perform similarity search for Multivariate Time Series （MTS） datasets, a distance - based index structure （Dbis） for similarity search was presented. The dimension of MTS database was reduced firstly by Principal Component Analysis （PCA）. The principal component of MTS was parted by cluster, and a MTS item was selected as reference point from each partition. The MTS items in each partition were transformed into a single dimensional space based on their similarity with respect to a reference MTS item. This allowed the MTS items to be indexed by using a B ＋ - tree structure. An extended Frobenius norm （Eros） was used to compare the similarity between MTS items. Several experiments on a financial MTS database were performed. The results show the effectiveness of Dbis.
出处
《计算机应用》
CSCD
北大核心
2008年第10期2541-2543,2552,共4页
journal of Computer Applications
基金
河北省科技攻关计划项目(062135140)
关键词
多变量时间序列
聚类
相似查询
Multivariate Time Series （MTS）
cluster
similarity search
