Characteristic-Based Clustering for Time Series Data

With the growing importance of time series clustering research, particularly for similarity searches amongst long time series such as those arising in medicine or finance, it is critical for us to find a way to resolve the outstanding problems that make most clustering methods impractical under certain circumstances. When the time series is very long, some clustering algorithms may fail because the very notation of similarity is dubious in high dimension space; many methods cannot handle missing data when the clustering is based on a distance metric. This paper proposes a method for clustering of time series based on their structural characteristics. Unlike other alternatives, this method does not cluster point values using a distance metric, rather it clusters based on global features extracted from the time series. The feature measures are obtained from each individual series and can be fed into arbitrary clustering algorithms, including an unsupervised neural network algorithm, self...

• Type: Journal
• Year: 2006
• Where: DATAMINE
• Authors: Xiaozhe Wang, Kate A. Smith, Rob J. Hyndman
• Keywords: Clustering Algorithm, DATAMINE 2006, Long Time Series, Time Series