对基于聚类和遗传算法的时间序列分割算法的改进

Vincent S.Tseng等人提出的基于聚类和遗传算法的时间序列分割算法中,对于适应值函数的定义存在缺陷,本文对此进行了改进:用归一化处理消除子序列幅度对距离计算的影响,并引入类间距使分割结果的类间差异(模式之间的差异)变得更明显。从对比算法改进前后的实验结果可以看出,这两点措施使适应值函数的精确性得到了提高,更有利于识别出子序列的模式。

Improvement of cluster-based genetic segmentation of time series algorithm

The fitness value function in the algorithm proposed by  Vincent S. Tseng et al based on cluster-based genetic segmentation of time series with DWT is inadequate. Two points on the calculation of fitness value of each chromosome was proposed to improve this algorithm: data normalization was used to eliminate the influence of amplitude,and the inter class distance was introduced to make distance between classes distinct. Experiments were conducted to compare the  former and improved algorithm,and the results showed that these two improvements improved the fitness value function accuracy which was more beneficial to identify sequence patterns.