  Tensor decompositions are used in various data mining applications from
social network to medical applications and are extremely useful in discovering
latent structures or concepts in the data. Many real-world applications are
dynamic in nature and so are their data. To deal with this dynamic nature of
data, there exist a variety of online tensor decomposition algorithms. A
central assumption in all those algorithms is that the number of latent
concepts remains fixed throughout the entire stream. However, this need not be
the case. Every incoming batch in the stream may have a different number of
latent concepts, and the difference in latent concepts from one tensor batch to
another can provide insights into how our findings in a particular application
behave and deviate over time. In this paper, we define "concept" and "concept
drift" in the context of streaming tensor decomposition, as the manifestation
of the variability of latent concepts throughout the stream. Furthermore, we
introduce SeekAndDestroy, an algorithm that detects concept drift in streaming
tensor decomposition and is able to produce results robust to that drift. To
the best of our knowledge, this is the first work that investigates concept
drift in streaming tensor decomposition. We extensively evaluate SeekAndDestroy
on synthetic datasets, which exhibit a wide variety of realistic drift. Our
experiments demonstrate the effectiveness of SeekAndDestroy, both in the
detection of concept drift and in the alleviation of its effects, producing
results with similar quality to decomposing the entire tensor in one shot.
Additionally, in real datasets, SeekAndDestroy outperforms other streaming
baselines, while discovering novel useful components.
