Biological applications of time series frequency domain clustering
Date
2012Source
Journal of Time Series AnalysisVolume
33Issue
5Pages
744-756Google Scholar check
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Clustering methods are used routinely to form groups of objects with similar characteristics. Collections of time series datasets appear in several biological applications. Some of these applications require grouping the observed time series data to homogeneous clusters. We review methods for time series frequency domain based clustering with emphasis on applications. Our point of view is that an appropriate notion of clustering for time series data can be developed by means of the spectral density function and its sample counterpart, the periodogram. For the development of frequency domain based clustering algorithms, it is required to define suitable similarity (or dissimilarity) measures. We review several such measures and we discuss various clustering algorithms in this context. Biological applications of time series frequency domain clustering are studied along with interesting complementary approaches. © 2011 Blackwell Publishing Ltd.