Abstract: This paper applied a modified algorithm of Ant Colony Optimization (ACO) for time series segmentation, which updated pheromone based on window according to the inner continuity of time series for improving optimization efficiency. The algorithm strengthened pheromone according to series continuity; thus, it can improve the positive feedbacks of ants. Then the positive feedbacks were a help for ants to choose paths in the next cycle. The experiments with true data sets validate that the modified method can accelerate the algorithm's convergence and reduce the segmentation cost to a certain extent.

Key words: data mining, time series, Ant Colony Optimization (ACO), linear representation, segmentation

摘要： 应用蚁群优化算法(ACO)对时间序列进行分割，为提高算法寻优效率，依据时间序列内在的连续性，采用信息素窗口式更新策略。依据序列连续性指导信息素进行窗口式的加强，从而使蚂蚁的正反馈机制得到增强，更利于蚂蚁的路径选择。实验结果表明，基于信息素窗口式更新策略的蚁群序列分割方法一定程度上可以加快算法收敛，同时可以有效地降低序列分割代价。

关键词: 数据挖掘, 时间序列, 蚁群优化算法, 线性表示, 分割