  Mining tasks over sequential data, such as clickstreams and gene sequences,
require a careful design of embeddings usable by learning algorithms. Recent
research in feature learning has been extended to sequential data, where each
instance consists of a sequence of heterogeneous items with a variable length.
However, many real-world applications often involve attributed sequences, where
each instance is composed of both a sequence of categorical items and a set of
attributes. In this paper, we study this new problem of attributed sequence
embedding, where the goal is to learn the representations of attributed
sequences in an unsupervised fashion. This problem is core to many important
data mining tasks ranging from user behavior analysis to the clustering of gene
sequences. This problem is challenging due to the dependencies between
sequences and their associated attributes. We propose a deep multimodal
learning framework, called NAS, to produce embeddings of attributed sequences.
The embeddings are task independent and can be used on various mining tasks of
attributed sequences. We demonstrate the effectiveness of our embeddings of
attributed sequences in various unsupervised learning tasks on real-world
datasets.
