Abstract: 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.

Key words: characteristics of time series, clustering, data mining, time series