  We propose a simple and efficient time-series clustering framework
particularly suited for low Signal-to-Noise Ratio (SNR), by simultaneous
smoothing and dimensionality reduction aimed at preserving clustering
information. We extend the sparse K-means algorithm by incorporating structured
sparsity, and use it to exploit the multi-scale property of wavelets and group
structure in multivariate signals. Finally, we extract features invariant to
translation and scaling with the scattering transform, which corresponds to a
convolutional network with filters given by a wavelet operator, and use the
network's structure in sparse clustering. By promoting sparsity, this transform
can yield a low-dimensional representation of signals that gives improved
clustering results on several real datasets.
