Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Biological applications of time series frequency domain clustering
AU - Fokianos, K.
AU - Promponas, V.J.
PY - 2012/9
Y1 - 2012/9
N2 - 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.
AB - 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.
KW - Distance measures
KW - macromolecular sequence analysis
KW - spectral analysis
KW - periodogram
KW - time‐course gene expression analysis
KW - time series
U2 - 10.1111/j.1467-9892.2011.00758.x
DO - 10.1111/j.1467-9892.2011.00758.x
M3 - Journal article
VL - 33
SP - 744
EP - 756
JO - Journal of Time Series Analysis
JF - Journal of Time Series Analysis
SN - 0143-9782
IS - 5
ER -