Algorithms of Data Mining and Knowledge Discovery of Correlativity in Two-Dimensional Time Series
Algorithms of Data Mining and Knowledge Discovery of Correlativity in Two-Dimensional Time Series
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DOI：
10.4028/www.scientific.net/amm.263-266.1844
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发表时间：
2012-12
期刊：
影响因子：
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通讯作者：
S. Hu;Ye-cheng Li;Wei Zhang-
中科院分区：
文献类型：
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作者：
S. Hu;Ye-cheng Li;Wei Zhang-
面向带有噪声干扰的复杂过程的动态数据，发现相关性和有序性知识非常困难。针对非平稳时间序列相关系数挖掘的不足，本文提出了一系列新的算法来挖掘二维时间序列的相关性。这些新算法基于可扩展的模型集框架。基于这些新的挖掘算法，列出决策表不仅可以挖掘二维时间序列中的相关性，而且可以发现深化的知识，将定性知识“非线性相对性”以及“非相对性”转化为更深层次的关于分析模型的定量知识。本文给出的这些新方法在框架上是公开的，可以通过其他新模型来丰富。这样就可以针对某些特定的专业领域进行一些专业的数据挖掘和知识发现。
Oriented at dynamic data from complicated process with noise disturbance, it is very difficult to discover knowledge of correlativity and orderliness. Following some analyzing results about the shortcoming of relative coefficients in mining non-stationary time series, a series of new algorithms are built in this paper to mine correlativity in two-dimensional time series. These new algorithms are based on a expansible framework of model set. Based on these new mining algorithms, a making decision table is listed not only to mine correlativity in two-dimensional time series, but also to discover deepening knowledge to transform the qualitative knowledge “nonlinear relativity” as well as “non-relativity” into deeper quantitative knowledge about analytical model. These new approaches given in this paper is exoteric in framework and can be enriched with additional new models. In this way, some professional data mining and knowledge discovery cab be fulfilled to aim at some specific professional fields.