相似性挖掘在时间序列数据中的应用研究 |
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引用本文: | 苏勇,都彬,胡昊.相似性挖掘在时间序列数据中的应用研究[J].人天科学研究,2011(10):142-144. |
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作者姓名: | 苏勇 都彬 胡昊 |
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作者单位: | 江苏科技大学计算机科学与工程学院,江苏镇江212003 |
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摘 要: | 针对时间序列的数据挖掘首先需要将时间序列(Time Series)数据转换为离散的符号序列(Symbol Sequence)。在前人的基础上,将界标模型和分段线性化进行了结合,以关键点作为分段依据,以最大似然函数和最小二乘法来拟合各分段线性拟合函数;此方法的优点在于符合人体生理实验结果,考虑了时间序列中的噪声。
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关 键 词: | 时间序列 相似性挖掘 线性化分段 关键点 |
Similarity Mining in the Application of Time Series Data |
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Abstract: | For the first time series data mining needs of the time series data into discrete sequence of symbols,in the previous article,based on the landmark model and the linear segments were combined to key point as the segment based on the maximum likelihood function and the least squares method to fit the piecewise linear function with the advantage in line with physiological results,considering the noise in time sequence. |
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Keywords: | Time Series Similarity Mining Linear Segments Key Points |
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