Effective similarity definition and clustering through time series evolution analysis
Chunhui Zhao · Computer Engineering and Applications Journal · 2008
Time series is one of the most widely-used data in business applications,e.g.power load sequence,web log etc.It is very important to mine time series for supporting decision-making.Especially,determining the similarity of time series plays a key part in various problems,e.g.analyzing the features of eletricity demand for each district.The previous methods,in the content of managing and mining data,hardly or do not enough use the evolution specialty of time series to measure similarity.This paper proposes an unexplored and effective approach based on evolution analysis of time series,and this approach quantifies the evolution trend to construct effective similarity definition,termed Similarity with Evolution Analysis(SEA).The clustering strategy based on SEA is also provided.The superior experimental results of compared methods on real data sets demonstrate the effectiveness of the method proposed,and thus imply the important significance of evolution analysis for similarity measure of time series.