时间序列灰色聚类模型 Grey clustering model of time sequence
它实现了两个专为短的时间序列聚类与聚类和自组织映射的原始算法。
It implements two original algorithms specifically designed for clustering short time series together with hierarchical clustering and self-organizing maps.
实验结果表明，LB_HUST不仅是时间序列基于DTW计算的一种紧密而对称的下界函数，而且在时间序列聚类中能够取得较好效果。
The experiments on the effects of LB_HUST show that, LB_HUST can be used as a compacted and symmetrical DTW lower bounding function, and has a better output in the time series clustering.
由于现实世界中时间序列多数是非线性的，而现有的时间序列聚类问题大多是基于线性时间序列模型进行聚类的，提出了可以用于非线性时间序列的聚类方法。
Most of the popular clustering methods are designed for the linear time series, assuming that the stationary time series can be fitted by linear model. In fact, the true word is nonlinear.
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