Time series segmentation is one of the many data mining tools. This paper, in French, takes local extrema as perceptually interesting points (PIPs). The blurring of those PIPs by the quick fluctuations around any time series is treated via an additive decomposition theorem, due to Cartier and Perrin, and algebraic estimation techniques, which are already useful in automatic control and signal processing. Our approach is validated by several computer illustrations. They underline the importance of the choice of a threshold for the extrema detection.

中文翻译：

---

通过极值进行数据挖掘和时间序列分割：初步调查

时间序列分割是众多数据挖掘工具之一。这篇法语论文将局部极值作为感知上的有趣点（PIP）。由于 Cartier 和 Perrin 以及代数估计技术（这些技术已经在自动控制和信号处理中有用），因此可以通过加法分解定理处理由任何时间序列周围的快速波动引起的这些 PIP 的模糊。我们的方法得到了几幅计算机插图的验证。他们强调了选择极值检测阈值的重要性。