《Clustering of time series data—a survey》.pdf

Pattern Recognition 38 (2005) 1857–1874 /locate/patcog Clustering of time series data—a survey T. Warren Liao∗ Industrial Manufacturing Systems Engineering Department, Louisiana State University, 3128 CEBA, Baton Rouge, LA 70803, USA Received 16 September 2003; received in revised form 21 June 2004; accepted 7 January 2005 Abstract Time series clustering has been shown effective in providing useful information in various domains. There seems to be an increased interest in time series clustering as part of the effort in temporal data mining research. To provide an overview, this paper surveys and summarizes previous works that investigated the clustering of time series data in various application domains. The basics of time series clustering are presented, including general-purpose clustering algorithms commonly used in time series clustering studies, the criteria for evaluating the performance of the clustering results, and the measures to determine the similarity/dissimilarity between two time series being compared, either in the forms of raw data, extracted features, or some model parameters. The past researchs are organized into three groups depending upon whether they work directly with the raw data either in the time or frequency domain, indirectly with features extracted from the raw data, or indirectly with models built from the raw data. The uniqueness and limitation of previous research are discussed and several possible topics for future research are identified. Moreover, the areas that time series clustering have been applied to are also summarized, including the sources of data used. It is hoped that this