  Recently the deep learning has shown its advantage in representation learning
and clustering for time series data. Despite the considerable progress, the
existing deep time series clustering approaches mostly seek to train the deep
neural network by some instance reconstruction based or cluster distribution
based objective, which, however, lack the ability to exploit the sample-wise
(or augmentation-wise) contrastive information or even the higher-level (e.g.,
cluster-level) contrastiveness for learning discriminative and
clustering-friendly representations. In light of this, this paper presents a
deep temporal contrastive clustering (DTCC) approach, which for the first time,
to our knowledge, incorporates the contrastive learning paradigm into the deep
time series clustering research. Specifically, with two parallel views
generated from the original time series and their augmentations, we utilize two
identical auto-encoders to learn the corresponding representations, and in the
meantime perform the cluster distribution learning by incorporating a k-means
objective. Further, two levels of contrastive learning are simultaneously
enforced to capture the instance-level and cluster-level contrastive
information, respectively. With the reconstruction loss of the auto-encoder,
the cluster distribution loss, and the two levels of contrastive losses jointly
optimized, the network architecture is trained in a self-supervised manner and
the clustering result can thereby be obtained. Experiments on a variety of time
series datasets demonstrate the superiority of our DTCC approach over the
state-of-the-art.
