基于Haar小波挖掘多个数据流的相关性
Mining Correlations between Multi-Streams Based on Haar Wavelet
在涉及多个流数据的应用中,流之间的相关性非常重要, 此文的工作包括 (1)引入了带过滤的全序概念,利用小波的压缩特性,描述数据流 (2)提出了等价模型来评价流数据的相关性,给出了三个关于小波系数和数据等价的定理 (3) 设计了抗干扰的滑动窗口算法来计算相关度 (4)在真实数据上的做了翔实的实验,表明在长窗口中,流数据的局部相关性不易受到噪声影响,实验也表明在小窗口环境中,新算法能很好地抵抗噪声、计算刘的相关性。
In the application with multiple data streams, the correlation between data streams is very significant. The main contributions of this paper included: (1) Introduces the concept of total ordering with filter and compression by wavelets to describe streams. (2) Proposes the equivalence model to evaluate correlations between streams, including three theorems about the equivalence between wavelet coefficients and original data about computing correlation. (3) Designs anti-noise algorithm with sliding windows to compute correlation measure. (4) Gives extensive experiments on real data, which show that the local correlations are hardly affected by data with noise in the long windows, and that new algorithm has well filter on the streams with noise in the environment of short size windows.
彭京、陈安龙、唐常杰、元昌安、胡建军
计算技术、计算机技术
相关系数, 多流数据, Haar小波倍长窗口
correlation coefficientmulti-streams dataHaar waveletdouble sliding windows.
彭京,陈安龙,唐常杰,元昌安,胡建军.基于Haar小波挖掘多个数据流的相关性[EB/OL].(2005-09-13)[2025-08-16].http://www.paper.edu.cn/releasepaper/content/200509-101.点此复制
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