  We present a data mining technique for the analysis of multichannel
oscillatory timeseries data and show an application using poloidal arrays of
magnetic sensors installed in the H-1 heliac. The procedure is highly
automated, and scales well to large datasets. The timeseries data is split into
short time segments to provide time resolution, and each segment is represented
by a singular value decomposition (SVD). By comparing power spectra of the
temporal singular vectors, singular values are grouped into subsets which
define fluctuation structures. Thresholds for the normalised energy of the
fluctuation structure and the normalised entropy of the SVD are used to filter
the dataset. We assume that distinct classes of fluctuations are localised in
the space of phase differences between each pair of nearest neighbour channels.
An expectation maximisation clustering algorithm is used to locate the distinct
classes of fluctuations, and a cluster tree mapping is used to visualise the
results.
