  Unsupervised learning is widely recognized as one of the most important
challenges facing machine learning nowa- days. However, in spite of hundreds of
papers on the topic being published every year, current theoretical
understanding and practical implementations of such tasks, in particular of
clustering, is very rudimentary. This note focuses on clustering. I claim that
the most signif- icant challenge for clustering is model selection. In contrast
with other common computational tasks, for clustering, dif- ferent algorithms
often yield drastically different outcomes. Therefore, the choice of a
clustering algorithm, and their pa- rameters (like the number of clusters) may
play a crucial role in the usefulness of an output clustering solution.
However, currently there exists no methodical guidance for clustering
tool-selection for a given clustering task. Practitioners pick the algorithms
they use without awareness to the implications of their choices and the vast
majority of theory of clustering papers focus on providing savings to the
resources needed to solve optimization problems that arise from picking some
concrete clustering objective. Saving that pale in com- parison to the costs of
mismatch between those objectives and the intended use of clustering results. I
argue the severity of this problem and describe some recent proposals aiming to
address this crucial lacuna.
