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Biological applications of time series frequency domain clustering
Author(s) -
Fokianos Konstantinos,
Promponas Vasilis J.
Publication year - 2012
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.2011.00758.x
Subject(s) - cluster analysis , series (stratigraphy) , data mining , correlation clustering , cure data clustering algorithm , mathematics , frequency domain , spectral clustering , context (archaeology) , computer science , similarity (geometry) , fuzzy clustering , clustering high dimensional data , time series , data stream clustering , pattern recognition (psychology) , algorithm , artificial intelligence , machine learning , geography , paleontology , mathematical analysis , biology , archaeology , image (mathematics)
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.