• Distance measures;
  • macromolecular sequence analysis;
  • spectral analysis;
  • periodogram;
  • time-course gene expression analysis;
  • time series

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.