  Conventional multi-view clustering seeks to partition data into respective
groups based on the assumption that all views are fully observed. However, in
practical applications, such as disease diagnosis, multimedia analysis, and
recommendation system, it is common to observe that not all views of samples
are available in many cases, which leads to the failure of the conventional
multi-view clustering methods. Clustering on such incomplete multi-view data is
referred to as incomplete multi-view clustering. In view of the promising
application prospects, the research of incomplete multi-view clustering has
noticeable advances in recent years. However, there is no survey to summarize
the current progresses and point out the future research directions. To this
end, we review the recent studies of incomplete multi-view clustering.
Importantly, we provide some frameworks to unify the corresponding incomplete
multi-view clustering methods, and make an in-depth comparative analysis for
some representative methods from theoretical and experimental perspectives.
Finally, some open problems in the incomplete multi-view clustering field are
offered for researchers.
