Piecewise two-dimensional normal cloud representation for time-series data mining
Piecewise two-dimensional normal cloud representation for time-series data mining
复制标题
用于时间序列数据挖掘的分段二维正态云表示
DOI：
10.1016/j.ins.2016.09.027
复制
发表时间：
2016-12
影响因子：
8.1
通讯作者：
Xu Ji
中科院分区：
文献类型：
--
作者：
Deng Weihui;Wang Guoyin;Xu Ji
已经提出了许多用于挖掘时间序列的高级降维方法，例如SAX、PWCA和基于特征的方法。由于低维时间序列数据挖掘的性能迅速下降以及不确定性时间序列数据量的不断增加，迫切需要开发新的时间序列表示，以在更低的缩减空间中保持良好的性能并有效地处理不确定性。在这项工作中，我们提出了一种新的时间序列表示，即基于云模型理论的二维正常云表示（2D-NCR）。该表示通过将原始时间序列转换为二维正常云模型序列来实现降维。在此基础上，提出了一种新的时间序列相似性度量方法。该方法既能反映时间序列的特征数据分布，又能捕捉到特征数据随时间的变化。我们在分类、聚类和按内容查询等各种数据挖掘任务上验证我们的表示的性能。实验结果表明，2D-NCR是一种有效且具有竞争力的时间序列数据挖掘方法。
Many high-level dimensionality reduction approaches for mining time series have been proposed, e.g., SAX, PWCA , and Feature-based. Due to the rapid performance degradation of time-series data mining in much lower dimensionality and the continuously increasing amount of time series data with uncertainty, there remains a burning need to develop new time-series representations that can retain good performance in much lower reduced space and address uncertainty efficiently. In this work, we propose a novel time series representation, namely Two-dimensional Normal Cloud Representation (2D-NCR), based on cloud model theory. The representation achieves dimensionality reduction by transforming the raw time series into a sequence of two-dimensional normal cloud models. Moreover, a new similarity measure between the transformed time series is presented. The proposed method can reflect the characteristic data distribution of the time series and capture the variation with time. We validate the performance of our representation on the various data mining tasks of classification, clustering, and query by content. The experimental results demonstrate that 2D-NCR is an effective and competitive representation for time-series data mining.
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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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