基于特征的时间序列聚类方法研究进展

时间序列聚类可以根据相似性将对象集分为不同的组, 从而反映出同组对象的相似性特征和不同组对象之间的差异特征。当序列维度较高时, 传统的时间序列聚类方法容易受噪声影响, 难以定义合适的相似性度量, 聚类结果往往意义不明确。当数据有缺失或不等长时, 聚类方法也难以实施。基于上述问题, 一些学者提出了基于特征的时间序列聚类方法, 不仅可以解决上述问题, 还可以发现序列本质特征的相似性。本文根据时间序列的不同特征, 综述了基于特征的时间序列聚类方法的研究进展, 并进行了分析和评述;最后对未来研究进行了展望。

Research Progress in Time Series Clustering Methods Based on Characteristics

As terabyte time series data pour into the world,more and more attentions have been paid to the technique of analyzing this data.To understand discrepancy between these data,time series clustering methods have been used to divide them into different groups by similarities.Due to high dimension of time series,the traditional clustering methods for static data is not valid for time series clustering problem when they are susceptible to noise,and can hardly define suitable similarity which are prone to a meaningless result.It is also vexatious for many other methods to solve the clustering problem with missing or unequal data.Time series clustering methods based on characteristics could deal with these problems and discover the essential similarities of time series in all directions.According to characteristics of time series,this paper aimed to review the research progress of characteristics-based clustering methods for time series.Firstly,we introduced the definition and classified the different characteristics of time series.Then we reviewed different time series clustering methods based on characteristics and summarized the generality of each method.Finally we discussed some deficiencies of existing methods,and predicted the future of the relative research.