  A popular tool for unsupervised modelling and mining multi-aspect data is
tensor decomposition. In an exploratory setting, where and no labels or ground
truth are available how can we automatically decide how many components to
extract? How can we assess the quality of our results, so that a domain expert
can factor this quality measure in the interpretation of our results? In this
paper, we introduce AutoTen, a novel automatic unsupervised tensor mining
algorithm with minimal user intervention, which leverages and improves upon
heuristics that assess the result quality. We extensively evaluate AutoTen's
performance on synthetic data, outperforming existing baselines on this very
hard problem. Finally, we apply AutoTen on a variety of real datasets,
providing insights and discoveries. We view this work as a step towards a fully
automated, unsupervised tensor mining tool that can be easily adopted by
practitioners in academia and industry.
